Validation and adaptation of the short-perceived food literacy scale (SPFL) among Israeli women

Validation and adaptation of the short-perceived food literacy scale (SPFL) among Israeli women | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Validation and adaptation of the short-perceived food literacy scale (SPFL) among Israeli women Keren L Greenberg, Donna R Zwas, Milka Donchin, Yael Bar-Zeev This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6644848/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Dec, 2025 Read the published version in BMC Public Health → Version 1 posted 10 You are reading this latest preprint version Abstract Background Food literacy (FL) encompasses the knowledge, skills, and behaviors required for making informed food choices. The short-perceived food literacy scale (SPFL, 29 items) is a widely used FL measurement tool, yet it has not been validated and adapted for the diverse Israeli population. This study aims to validate and shorten an adapted SPFL for Hebrew and Arabic-speaking women in Israel, ensuring cultural relevance and reducing respondent burden. Methods The validation process comprised three steps: face validity and pretesting of an extended 35-item SPFL, content validation via confirmatory factor analysis (CFA) and exploratory factor analysis (EFA) on survey data including 2,129 participants (653 Arabic speakers, 1,476 Hebrew speakers), and convergent validity assessment through correlation with the Israeli Mediterranean Diet Adherence Scale (I-MEDAS). Reliability was assessed via internal consistency measures, and associations between FL levels and socio-demographic factors were also examined. CFA confirmed the original SPFL’s 8-domain structure, while EFA identified six FL domains, leading to a refined 23-item modified SPFL (M-SPFL). Results The M-SPFL demonstrated strong internal consistency (composite reliability = 0.89) and acceptable model fit across both language groups, and was correlated with the original SPFL (r = 0.96, p < .001) and with I-MEDAS scores (r = 0.52, p < .001). FL levels were positively associated with age, marital status, and higher education. Conclusions The M-SPFL is a valid, reliable, and culturally adapted tool for assessing FL among Israeli women. Its application can enhance public health initiatives by informing targeted nutrition interventions to improve dietary behaviors and reduce health disparities. Trial registration Not applicable. Food literacy Scale validation Mediterranean diet Public health nutrition Women’s nutrition Figures Figure 1 Figure 2 Figure 3 1. Background The concept of food literacy (FL) has emerged in recent years to address the multiple skills, competencies, and external factors that affect an individual's ability to make healthy nutritional choices. One widely accepted definition of FL, offered by Vidgen and Gallegos [ 1 ], and often cited when referring to FL in the literature, is: " the scaffolding that empowers individuals, households, communities and nations, to protect diet quality through change and strengthen dietary resilience over time. It is composed of a collection of inter-related knowledge, skills and behaviours required to plan, manage, select, prepare and eat food to meet needs and determine intake " [ 1 ]. The authors identified 4 dimensions of FL, focused on individual skills and abilities: (1) planning and managing, (2) selecting, (3) preparing, and (4) eating [ 1 ]. Cullen et al. [ 2 ] added that FL should also consider environmental, social, economic, cultural, and political components [ 2 ], and in the following years many others have added additional definitions to the FL concept [ 3 ]. Multiple frameworks have been developed in the last decade, emphasizing different constructs [ 4 – 6 ]. Despite a variety of definition and frameworks, common themes appear in many FL related papers. In 2017, Truman et al. examined 38 novel FL definitions and found 6 common themes integrated in these conceptualizations: knowledge, emotions, skills/behaviours, health/food choices, culture and the broader food system [ 7 ]. The variety of definitions, frameworks and models for FL has resulted in a variety of measurement tools [ 8 ], while there is currently no consensus on one specific tool. A recent scoping review from 2019 that aimed to identify existing tools that measure FL in adults [ 8 ] found a total of 12 different tools. Poelman's short perceived FL scale (SPFL)[ 9 ], based on the Vidgen and Gallegos model [ 1 ], is one of five scales recommended by this review [ 8 ]. The SPFL has been used multiple times and validated in various languages [ 10 – 16 ]. Since the 2019 review, additional studies describing novel FL measuring instruments for adults have been published [ 17 – 24 ], most of which do not capture all of the Vidgen and Gallegos domains and have been scarcely used. The SPFL consists of 29 items that cover eight domains of FL: food preparation skills, resilience and resistance, healthy snacking, social and conscious eating, examining food labels, daily food planning, healthy budgeting and healthy food stockpiling [ 9 ]. A higher mean SPFL score was correlated with healthier food consumption [ 9 ], and specifically with adherence to the Mediterranean diet [ 11 , 25 ]. However, the SPFL does not include several items that may be important and are included in other tools, such as those that reflect shopping preparations [ 19 , 22 , 23 , 26 , 27 ], portion size [ 24 ], intentionally planning/consuming healthy meals [ 17 – 19 , 22 – 24 , 26 – 28 ], or altering a recipe to make it healthier [ 19 , 26 ]. Additionally, culture is included in several tools [ 20 , 22 – 24 ] while it is absent in the SPFL. Moreover, the SPFL does not include certain aspects of FL that have emerged in recent years, such as sustainability conscientiousness [ 18 , 20 – 22 ], whether referring to food waste/food related waste, methods of agricultural and livestock production, transportation and distribution, or awareness of food decisions impact on environment and climate sustainability. In Israel, the need to target and measure FL skills and behaviours is apparent from studies presenting prevalence of factors associated with reduced FL, including low rates of reported nutritional label examination [ 29 ] and eating family meals [ 30 ], high rates of reported purchase of processed food [ 29 ], eating out of boredom and eating while watching TV or working [ 30 ]. These findings, in addition to the high prevalence of risk factors among Israelis [ 31 , 32 ], emphasize the importance in utilizing a validated culturally-adapted FL measuring tool to map, monitor and intervene in this field. An adapted (yet not validated) version of the SPFL has been previously used in Israel in Hebrew and Arabic, evaluating a FL intervention in the community [ 25 ]. The results indicated an increase in FL levels post intervention and correlation between FL and healthy eating habits. However, the study also found that participants perceived the survey as too long to fill out, which affected post intervention survey completion rates. 2. Methods This study aims to validate and shorten a FL assessment scale based on the SPFL survey among Hebrew and Arabic speaking women in Israel. The study focuses on women as they are more likely to be the household dietary gatekeepers [ 33 , 34 ] (i.e., do the majority of grocery shopping, meal planning and cooking). Dietary gatekeepers hold a major influence on their family's diet, and act as a significant determinant of the food consumed in their home [ 35 ], and several FL tool validation studies included mainly women [ 9 , 11 , 15 , 28 , 36 ]. For the validation of the FL tool, this study used the SPFL survey with an additional 6 questions reflecting aspects absent in the original SPFL (Supplement 1), including items concerning culture, shopping preparations, portion size, intentionally consuming healthy meals, altering a recipe to make it healthier and food waste. This study's secondary objective is to explore associations between socio-demographic variables and FL levels among Hebrew and Arabic-speaking Israeli women. 2.1. Procedure The validation process was comprised of three steps: the first step included assessment of face validity and a survey pretest in target communities. The second step included an online survey to test content validity in a large sample, by conducting both a confirmatory factor analysis (CFA) on the original 29 SPFL items in order to confirm the fit of the model and its domains in the Israeli sample, and an exploratory factor analysis (EFA) to examine the integration of the 6 additional items in the model. This stage also included reduction of questions that do not add to the validity of the tool in order to shorten the survey. The final factor model was then confirmed using a second CFA. Reliability tests were performed to examine the internal consistency of the final scale and within each of its domains. The third step included convergent validity, testing the correlations between the SPFL scale and the Israeli Mediterranean Diet Adherence Scale (I-MEDAS) scale. The Mediterranean diet is the recommended diet in Israel, associated with decreased obesity, diabetes, cardiovascular disease, dementia and cancer [ 37 ]. In addition to the validation process mentioned above, associations between socio-demographic variables and FL levels were examined to elucidate the construct and its predictors. 2.2. Step 1: Face Validity and Pretest Prior to face validity, the 29-item SPFL survey was translated into Hebrew and Arabic using two forward translators, one of which is a certified translator and the other a bilingual researcher, and a third translator independently checked the forward translation against the original questionnaire and resolved any discrepancies. An additional six questions were added based on validated measures and adapted to the target population (Supplement 1). This adapted extended SPFL (35-items) underwent face validation and pre-testing: the face validity process included sending the questionnaire to five nutrition/health literacy experts (including two Arabic speakers), who replied with feedback on the scale, including its cultural appropriateness, and its fitness/representation of the FL framework. The experts received the FL definition and the Vidgen and Gallegos model the SPFL is based on in order to better gauge the fitness of the scale. Next, the questionnaire was pretested both in Hebrew and in Arabic among 24 women from different backgrounds and education levels through existing community groups, to test readability and understandability. After each of these steps, the survey was amended as necessary. The amendments included slight rephrasing and text changes, though no major alteration to the original text were necessary. 2.3. Step 2: Content validation 2.3.1. Design An online cross-sectional survey conducted between March-July 2024. 2.3.2. Study Sample and Recruitment The sample size was calculated following guidelines for the respondent-to-item ratio, ranging from 5:1 to 30:1 [ 38 ] (i.e., five to thirty respondents per every item on the scale to be validated), with the accepted "rule of thumb" between 5:1 to 10:1 ratio [ 39 ]. In this study we used a 10:1 ratio, resulting in a sample size of 350 in each population (Arabic and Hebrew speaking) for the validation of the 35-item survey. To assure this sample size is large enough to detect the correlation between FL and adherence to the Mediterranean diet, a sample size was calculated using the "PS Power and Sample Size" calculator, based on data from a study describing correlation between the measures (SD x = 0.44, SD y = 1.7, β = 1.51) [ 39 ], using 5% significance level, and 90% power, and resulting in a sample size of 60, well within the calculated sample size described above. Participants were recruited through Facebook advertisements in Hebrew and Arabic that were promoted to target adult women (over the age of 25, as younger women are less likely to be the dietary gatekeepers) from different areas in Israel (90% of Israelis over the age of 20 use the internet [ 40 ], 75% are active on Facebook [ 41 ]), with the incentive of enrolment in a prize-winning raffle worth 300 NIS (~ $ 80 USD). After surpassing the initial sample size, preliminary data analysis revealed most respondents to be of higher education levels. Therefore, additional sampling took place targeting women with lower education levels aiming to receive a more representative sample and to enable sub-analyses of this group (in Israel 40% of women have academic degrees, 55% have high school diplomas or lesser education) [ 42 ]. Respondents who were not female, were under the age of 25, or who were not Israeli citizens or residents, were navigated out of the online survey before moving forward. An additional open question at the end of the survey inquired about current place of residence; respondents who do not reside in Israel at the time of survey response were not included in the final sample. As this survey utilized social media recruitment, a predefined protocol to eliminate duplicate and invalid survey responses was employed [ 43 – 45 ]. This included manual screening for internal inconsistencies, low differentiation of responses, high level of item unresponsiveness, and exclusion of respondents who answered an attention check question incorrectly. Only respondents who completed 100% of the extended SPFL and I-MEDAS scales and who correctly answered the attention check question were included in the final sample. For respondents with multiple entries, only the first or complete entry were included. The final sample included n = 2129 respondents (653 Arabic speakers, 1476 Hebrew speakers), see flowchart in Fig. 1 . 2.3.3. Study Measures The online survey contained the translated culturally-adapted extended SPFL scale (after face validation and pre-testing in step 1), the I-MEDAS scale [ 46 ], and socio-demographic questions. The extended SPFL survey consisted of 35 questions rated on a 5-point Likert scale ("never" to "always" / "not at all" to "to a very large extent", calculated as an average score) (Supplement 1). The I-MEDAS, adapted from the Mediterranean diet adherence screener (MEDAS) [ 47 ], consists of 17 questions and is scored based on a pre-specified scoring method (range 0–17). The I-MEDAS has been validated in both Hebrew and in Arabic, with improved scores associated with reduced mortality risk [ 46 ]. Additionally, data regarding socio-demographic variables were collected, including year of birth, country of birth, education level, marital status, number of children and place of residence. 2.3.4. Data cleaning The response distribution for each of the 35 questions was examined, and questions where 60% or more of the responses were in the lower or higher ends of the Likert scale (i.e. answers 1 or 5) were dropped, as they do not contribute to the variance of the scale [ 39 ]. This procedure identified one question, question 29 of the original SPFL scale, with 61.6% of respondents answering '5' on the Likert scale. The question was subsequently dropped from the final scale analysis, which included 34 items. Additionally, individual responses were examined in order to identify respondents who supplied the same response on the Likert scale across 30 or more of the 35 SPFL items, as these respondents do not add the variance of the sample and are suspected to be unauthentic [ 39 ]. Nine such respondents were identified in this process and removed from the final analysis. 2.3.5. Data Analysis 2.3.5.1. Descriptive statistics Descriptive statistics, including proportions for categorical variables, and mean and standard deviation (SD) for continuous variables, were used to assess sociodemographic data. 2.3.5.2. Confirmatory (CFA) and Exploratory factor analysis (EFA) In order to perform reliable analyses, we divided the sample into two randomly selected adequately-powered subsamples for training (exploratory) and testing (confirmatory). This division was conducted to test and confirm the fit of the suggested structure on another random set of data [ 48 , 49 ]. The CFA was first performed to test the original 29-item SPFL division into the eight domains presented by Poelman et al.[ 9 ]. This test aimed to determine the extent to which our empirical data matched with the original instrument. The test was conducted on the training sample, and then confirmed on the testing sample. The CFA model fit was determined using the comparative fit index (CFI, levels over 0.9 indicate a fair model fit, level over 0.95 indicate a good model fit [ 48 , 50 ], root mean square error of approximation (RMSEA, levels under 0.06 indicate a good fit [ 50 ] and standardized root mean square residuals (SRMR, levels under 0.08 indicate a good fit [ 50 ]. Next, EFA was conducted to test the integration of the six new items in the SPFL tool, and to explore whether the tool can be shortened by eliminating items that do not fit or contribute to the new-found domains. To that extent, the EFA was performed initially without rotations and items with poor loading (λ < 0.40) or multiple loading[ 51 ] were excluded during the EFA analysis. Next, the EFA was performed with rotation in order to place the remaining items in the identified domains. In this analysis principal axis factoring (PAF) was used as the extraction method, with the Promax (oblique) rotation method [ 39 ]. The Kaiser-Meyer-Olkin coefficient (KMO, considered reasonable if over 0.6, and good if over 0.8) and Bartlett’s Test of Sphericity (considered good if significance is under 0.05) determined data adequacy for EFA [ 36 ]. Finally, to assess how distinct each new domain is compared to the others, the average variance extracted (AVE) was calculated and compared to the maximum shared variance (MSV), which is the square of the highest correlation found between each domain and every other domain. The common threshold for AVE is greater or equal to 0.50, but more importantly the AVE is expected to be greater than the MSV, which indicates a discriminant validity for all domains [ 52 ]. 2.3.5.3. Reliability After conducting both CFA for the original SPFL and EFA for the extended SPFL (resulting in a shorter modified tool), internal consistency was determined for both scales and for each domain within the scale using composite reliability (CR) as a measure of percent shared variance across domain items (squared sum of factor loadings over the sum of the observed item variance plus the former [ 53 ], which resembles McDonald’s Omega [ 54 ]. A CR value greater or equal to 0.7 is considered an indicator of good internal consistency. 2.3.5.4. Hebrew/Arabic confirmation In order to confirm whether the modified SPFL (M-SPFL) is a good fit for both the Hebrew and Arabic speaking populations, a CFA of the final tool was performed separately for each sub-population. Additionally, a multi-group comparison was conducted to measure invariance across the six domains between Hebrew and Arabic speakers. 2.4. Step 3: Convergent validity This step included testing the convergent validity of the M-SPFL scale by examining the Spearman correlations between the M-SPFL and the SPFL scores, and the M-SPFL and the I-MEDAS scores on the data collected as part of step 2. 2.5 Associations between socio-demographic variables and FL levels After validation of the tool, the M-SPFL was treated as a continuous scale (scored by its mean) for further analyses. Additionally, the M-SPFL was divided into 3 levels, based on the Likert scale (negative answers (1–2), neutral (3) and positive answers (4–5)): inadequate (1.00 to 2.49 points), problematic (2.50 to 3.49 points), sufficient (3.50 to 5 points). This division is similar to that presented by Kolpatzik and Zaunbreche [ 55 ]. Both the M-SPFL mean score and category proportions were used to assess FL level in the study sample. ANOVA was used to test differences in the mean M-SPFL and I-MEDAS scores between sectors, and the chi-square test was used to test differences in category proportion between sectors. Pearson and Spearman tests were conducted to examine associations between socio-demographic continuous and ordinal variables with FL levels, as well as ANOVA tests for categorical variables. The general linear model was performed to test the associations in a single model and included all variables that were suggested to be associated with M-SPFL (p < .05) in the bivariate analyses. The same model was used a second time to examine these associations in each sector separately. Additionally, a multinomial regression was conducted examining associations between the variables and M-SPFL level as a categorial variable. 3. Results 3.1. Online survey demographics The final sample of the online survey included n = 2129 women, with a mean age of 44 ± 11.8. Of the respondents, 72.3% (n = 1538) held an academic degree, 72.4% (n = 1539) were married with a mean number of children of 2.19 ± 1.6, 68.2% (n = 1445) were Jewish and 26.3% (n = 558) were Muslim (Table 1 ). Table 1 Distribution of the online survey respondents by demographic characteristics (n = 2129) Variable Total (N = 2129) Hebrew (N = 1476) Arabic (N = 653) Age (mean, SD) 44.2 (± 11.8) 45.3 (± 12.6) 41.6 (± 9.5) Number of Children (mean, SD) 2.2 (± 1.6) 1.8 (± 1.5) 2.9 (± 1.6) Education Level n (%) No certificate/ high school certificate/matriculations 331 (15.6%) 192 (13%) 139 (21.3%) Vocational degree 225 (12%) 142 (9.6%) 113 (17.3%) Academic degree 1538 (72.3%) 1141 (77.3%) 397 (60.9%) Marital Status Married 1539 (72.4%) 979 (66.3%) 560 (86.3%) Divorced/ Widowed 213 (10%) 166 (11.3%) 47 (7.3%) Single 371 (17.5%) 329 (22.3%) 42 (6.5%) Religion Jewish 1445 (68.2%) 1445 (97.9%) 0 (0.0%) Muslim 558 (26.3%) 8 (0.5%) 550 (85.4%) Christian 91 (4.3%) 6 (0.4%) 85 (13.2%) Druze 6 (0.3%) 0 (0.0%) 6 (0.9%) Other* 20 (0.9%) 17 (1.2%) 9 (1.4%) *'Other' religion responses include: Circassian, Atheist, no religion, refuse to answer. 3.2. Content validation As mentioned previously, for the validation analyses there were 9 respondents who did not add to the tool variance and therefore they were not included in subsequent analysis (final sample N = 2120). CFA analysis testing the original 29-item SPFL, initially performed in the training subsample (N training =1,060), indicated that the model has an acceptable level of fit: CFI = .930; RMSEA = .046, 95%CI [.043,.049]; SRMR = .045. These results were consistent for the testing subsample (N testing =1,060): CFI = .943; RMSEA = .040, 95%CI [.037,.044]; SRMR = .050. The CFA showed similar results indicating acceptable level of fit for both the Hebrew and Arabic speakers’ subsamples; N training =1,060: N Hebrew =737: CFI = .946, RMSEA = .039, 95%CI [.035,.043]; SRMR = .041; N Arabic =323: CFI = .929; RMSEA = .046, 95%CI [.039,.052]; SRMR = .058. These results remained consistent in the testing sub-sample, N testing =1,060: N Hebrew =736: CFI = .932; RMSEA = .046, 95%CI [.042,.050]; SRMR = .057; N Arabic =324: CFI = .930; RMSEA = .044, 95%CI [.037,.050]; SRMR = .062 EFA was performed on the training sub-sample, and confirmed on the testing sub-sample. The KMO coefficient was 0.88 and Bartlett's test was statistically significant (p < 0.001), confirming suitable data adequacy for EFA. The EFA, performed on the expanded 34-item SPFL, had an initial cumulative loading percent of 46.4% across 8 factors. In this EFA, nine items had a loading factor below 0.4, and were therefore removed. The EFA was performed again for the 25 remaining items, and while the cumulative loading percent went up to 50.2% (across six factors), 2 items had a loading factor below 0.4 and were removed. The final EFA was then performed for the 23 remaining items, the cumulative loading percent went up to 52.2% (across six factors), and all items had sufficient loading factors. Next, the EFA was performed using Promax (oblique) rotation, placing the 23 items into the six factors. The item division matched the original 8 SPFL domains, apart from one domain (social and conscious eating) that was fully omitted in the EFA process, and two domains that appear separately in the original SPFL but were combined in this analysis (examining food labels and daily food planning). Factor correlation matrix found that the factors were not highly correlated with each other (the highest correlation being 0.6), confirming they represent six separate domains. Four of the six new questions remained and were appropriately integrated in the domains. The remaining six domain themes are: food preparation skills (4 items), resilience and resistance (5 items), healthy snack styles (4 items), examining food labels and daily food planning (6 items), healthy budgeting (2 items), healthy food stockpiling (2 items). The final 23-item M-SPFL appears in Supplement 2. CFA showed an acceptable level of fit for these six domains (N testing =1060): CFI = .946; RMSEA = .050, 95%CI [.046,.054]; SRMR = .044 (Fig. 2 ). Table 2 presents the AVE and MSV for each domain. Although the AVE values for four domains were below the common threshold for AVE (AVE ≥ .50), these values are higher than their respective MSV values, which indicated a discriminant validity for these domains, i.e., the distinguishability of the six extracted domains from one another. The rectangles in this figure indicate the 23 items of the scale (numbered according to the original scale), and the six directly connected ellipses represent the six factors. The lines between the factors and the items represent the causal effects, and each shows the standardized factor loadings of each item for its "correlated factor". The numbers to the left of the items are the item standardized residual variance. The errors between the factors represent covariance. Domain 1 = Food preparation skills, Domain 2 = Resilience and resistance, Domain 3 = Healthy snack styles, Domain 4 = Examining food labels and daily food planning, Domain 5 = Healthy budgeting, Domain 6 = Healthy food stockpiling 3.2.1. Reliability The internal consistency for both the original SPFL (CR = 0.85) and the 23-item M-SPFL (CR = 0.89) were high, and each of the six new domains showed good reliability as well with a CR of over 0.7 (Table 2 ). Table 2 M-SPFL domain characteristics (n = 2120) Domain Number of Items CR. [95%CI] AVE MSV Mean SD Food preparation skills 4 .780 [.752,.803] .488 .164 4.01 0.74 Resilience and resistance 5 .760 [.734,.783] .389 .267 3.23 0.73 Healthy snack styles 4 .756 [.723,.782] .439 .267 3.33 0.86 Examining food labels and daily food planning 6 .806 [.775,.830] .410 .251 3.30 0.85 Healthy budgeting 2 .839 [.810,.863] .702 .189 3.85 0.88 Healthy food stockpiling 2 .824 [.785,.852] .703 .007 3.38 1.12 CR-composite reliability; AVE-average variance extracted; MSV-maximum shared variance, Domain range: 1–5. 3.2.2. Hebrew/Arabic confirmation CFA results show that both Hebrew and Arabic subpopulations show an acceptable level of fit for the M-SPFL model, (N testing =1060): (N Hebrew =736): CFI = .938; RMSEA = .055; SRMR = .049; and (N Arabic =324): CFI = .905; RMSEA = .065; SRMR = 0.06. The multi-group comparison of the six domains showed that differences between the groups are only at scale level (strong-scalar invariance) but not in the factor loadings (weak-metric invariance), i.e., both populations have similar factor loadings, that is, they answered in the same pattern, but differ in their measured M-SPFL level in each domain. Thus, the division into the six construct domains represents the two groups similarly (Supplement 3). 3.3. Convergent validity The mean M-SPFL score in this sample was 3.47 ± 0.58 (3.46 ± 0.57 for Hebrew speakers, 3.49 ± 0.61 for Arabic speakers, p = .232), and the mean I-MEDAS score was 9.01 ± 2.16 (9.15 ± 2.22 for Hebrew speakers, 8.69 ± 2.00 for Arabic speakers, p < .001). Pearson correlations analyses show the M-SPFL and SPFL were highly correlated (r = .957, p < .001). Additionally, there was a positive correlation between the M-SPFL and the I-MEDAS (r = .52, p < .001), similar to the correlation found between the original 29-item SPFL and the I-MEDAS (r = .51, p < .001). These results remained the same when testing for each sector separately. 3.4. Associations between socio-demographic variables and FL levels Bivariate analyses found that age, education, marital status and number of children were associated with the M-SPFL score (p < 0.05 for all). The general linear model (using univariate ANOVA) including all the above socio-demographic variables in addition to sector (Table 3), mostly reinforced these results with a few exceptions - Hebrew speakers had lower M-SPFL scores (B = − .074, 95% CI [-.013-(-).016], p = .013) compared to Arabic speakers. Having an academic or vocational degree compared to matriculations or less (B = .0152, 95% CI [.083-.221], p < 0.001; B = .164, 95% CI [.071-.257], p < .001, respectively), being married (B = .088, 95% CI [.010-.166], p = .026) compared to single, and age (B = .010, 95% CI [.007-.012], p < 0.001) were positively correlated with higher M-SPFL scores. Exploring the associations in each sector separately revealed similar results: among Hebrew speakers, being married (compared to single), having an academic or vocational degree (compared to matriculations or less) and age were correlated with increased M-SPFL scores. Among Arabic speakers having an academic degree (compared to matriculations or less) and increased age were correlated with increased M-SPFL scores (p < .001) (Supplement 4). Table 3. Associations between M-SPFL and socio-demographic variables, general linear model results Parameter Mean M-SFPL (SD) B Std. Error 95% CI p-value Sector Hebrew Speakers 3.44 (.56) − .074 .030 − .013-(-).016 .013 *Arabic speakers 3.49 (.61) (ref.) Marital status Married 3.49 (.57) .088 .040 .010-.166 .026 Divorced/Widowed 3.54 (.59) .080 .053 − .024-.184 .130 *Single 3.33 (.59) (ref.) . . Education Academic degree 3.48 (.57) .152 .035 .083-.221 < .001 Vocational degree 3.54 (.63) .164 .047 .071-.257 < .001 *Matriculations or less 3.36 (.56) (ref.) . . Age .010 .001 .007-.012 < .001 Number of children − .009 .010 − .029-.011 .390 *Reference category Pairwise comparisons showed no significant difference between 'Married' and 'Divorced/ Widowed' categories (p = .857), and between 'Academic degree' and 'Vocational degree' (p = .758) Dividing the M-SPFL into levels showed that approximately 50% of the sample had sufficient levels of FL, and 50% had problematic or inadequate levels (i.e., non-sufficient levels) (Fig. 3 a). No difference was seen between sectors (p = .449, Fig. 3 b). Multinomial regression showed that age and being married were predictors for inadequate FL levels, and Arab sector, increased age, and lower education level are predictors for problematic FL levels, both in comparison to sufficient FL levels (Supplement 4). 4. Discussion This study aimed to validate and shorten an extended SPFL scale in both Hebrew and Arabic. This process resulted in the M-SPFL, a 23-item scale covering 6 distinct FL domains: food preparation skills, resilience and resistance, healthy snack styles, examining food labels and daily food planning, healthy budgeting, and healthy food stockpiling. The M-SPFL demonstrated good internal consistency and acceptable model fit across both Hebrew and Arabic-speaking populations, suggesting its utility as a culturally appropriate tool for measuring FL in Israel's diverse population, while reducing respondent burden. The validation process included face validity and pretesting of an extended 35-item SPFL scale (original 29-item SPFL and additional 6 new items) as the first step, followed next by content validation. The 29-item original SPFL showed acceptable levels of fit (shown by CFA) in both Hebrew and Arabic speaking sub-populations. This indicated that the original scale is compatible with the conceptual structure. The subsequent exploratory process aimed to explore the properties and factor division of the original construct among Israeli women, as well as to shorten the tool. During this process, 12 items were removed, including an entire domain from the original SPFL, social and conscious eating. Although social and conscious eating plays an important role in the concept of FL [ 2 , 5 , 56 ], this study indicates that Israeli women do not perceive it as part of the content realm of the rest of the scale items, viewing it as a different construct, as expressed by the low loading factors of the domain items. This is similar to the findings of Luque et al.’s Spanish SPFL validation study [ 11 ], which also eliminated the social and conscious eating domain due to low significance. Luque et al. explained this as a result of the characteristics of their study population (busy and time restricted academic university students), but also highlighted the importance of geography and culture differences on this concept [ 7 ]. It is also important to note that in the original SPFL development and validation study, this domain had a Cronbach’s alpha just below the acceptable value of 0.7, and this was also seen in a Turkish SPFL validation study (domain Cronbach’s alpha = 0.61)[ 12 ]. However, in our professional opinion, social and conscious eating is an important factor that has an effect on eating habits and FL level. Therefore, we suggest adding at least one question from this domain (question 19, Supplement 1) as a covariate, when administering questionnaires measuring FL levels in Israeli women. A further difference in the domains, in addition to the elimination of the social and conscious eating domain, was the merging of two domains- examining food labels and daily food planning. This restructuring of factors was also seen in Luque's study, who chose to name this combined domain ‘nutritional literacy and planning’. Although understanding of food labelling information appears in the ‘Select' domain of the Vidgen Gallegos model and not the ‘Plan and Manage domain [ 1 ], one can understand how the selection of healthier products can also be seen as part of planning and prioritizing healthy food intake. Of the 6 new items added to the original 29-item scale, 4 remained and were placed appropriately in the 6 domains. CFA showed acceptable levels of fit for the 23-item M-SPFL, for the whole sample and for each sector. The internal consistency showed adequate reliability for each of the domains and for the overall scale (CR = 0.89, slightly higher than that of the 29-item scale, CR = 0.85), and was comparable to the level of internal consistency observed in other food literacy scales (Cronbach's alphas of 0.83 [ 9 ], 0.82 [ 28 ], and 0.89 [ 24 ]). The mean score of the 23-item M-SPFL (3.47 ± 0.58, scale range 1–5), as well as the mean score of the 29-item SPFL (3.55 ± 0.48), in our sample was slightly lower than that seen in the original SPFL validation sample [ 9 ] (3.833.47 ± 0.41), even though the samples are similar in gender, age, and education level. This may suggest that the lower FL levels measured in the current study are due to core population disparities, stemming from culture, the Israeli physical and social environment and policy differences. Finally, the M-SPFL showed positive correlation with adherence to the Mediterranean diet (measured using the I-MEDAS [ 46 ]), consistent with previous studies [ 11 , 25 ], indicating its convergent validity. This study measured FL levels among a large number of diverse Israeli women, providing stakeholders with the information to make informed public health policies according to sub populations needs regarding healthy nutrition. Approximately 50% of respondents had insufficient FL levels, which highlights the need for action. The increased FL levels with age, seen in other studies as well [ 13 , 55 ], suggests that FL skills may develop over time through accumulated experience. The association between higher education and increased FL scores, were also similar to findings in other studies, [ 55 , 57 ] and highlight potential disparities in food-related knowledge, skills and opportunities that merit attention from nutrition and public health professionals. The finding that married women scored higher than single women may reflect the influence of family responsibilities on food-related behaviors and skills development, or just a greater opportunity to put such skills to use. Notably, among Arabic speakers no significant correlation was seen between marital status and M-SPFL, but this may be due to the small number of unmarried participants in this sub-group (only 42 singles). While bivariate analyses showed no significant differences between the Arabic and Hebrew speaking populations when examining both the mean score and the categorization to M-SPFL levels, regression models showed slightly higher scores for Arabic speakers after adjusting for socio-demographic variables. When examining each FL domain between sectors, the M-SPFL performed similarly across Hebrew and Arabic-speaking populations at the construct level, though differences emerged at the scale level. This suggests that whereas the tool measures the same underlying constructs in both populations, there may be cultural variations in how these skills and behaviors manifest. As the M-SPFL measures perceptions of FL skills and not actual behaviours, there may be a tendency among the Arabic sector to positively view their FL related behaviours, even though they report lower adherence to the Mediterranean diet (I-MEDAS scores are slightly lower among Arabic speakers, p < .001). In general, Israeli Arabs tend to report higher levels of self-rated health compared to Israeli Jews [ 58 ]. Several Israeli studies showed that Arab participants reported higher self-rated health, although their long-term survival rate is significantly lower and they are disadvantaged according to most health indicators [ 58 , 59 ]. This discrepancy has been previously explained [ 60 ] by suggesting the two sectors may not have the same meaning in relation to objective measures of health, as cultural differences may play a role in health perceptions of different populations. This may explain the gap between reported perceived nutritional-related behaviours and actual food consumption. This finding once again underscores the importance of considering cultural context when developing and implementing FL interventions and policies [ 25 ]. 4.1. Strengths and Limitations Several limitations of this study should be noted. The social media recruitment strategy may have resulted in selection bias, particularly toward more educated participants, despite efforts to recruit across various education levels. Additionally, self-reported measures may be subject to bias. As this study focused on women over 25 years old, this study cannot be generalized to younger ages or to men. Investigation of FL among other population groups, including men and young adults, will provide valuable additional insights and is recommended as the next step. The study also has several strengths, including its large sample size, robust validation process, and inclusion of both Hebrew and Arabic-speaking populations while ensuring cultural sensitivity through the face validity and pretest process. Finally, the use of a validated tool enables future comparisons with other international studies. Future research should examine the M-SPFL's predictive validity for dietary behaviors and health outcomes, as well as its sensitivity to change in intervention studies. 5. Conclusions The 23-item M-SPFL represents a valid and reliable tool for measuring FL among Israeli women, with potential applications in both research and practice. This shorter version of the 29-item SFPL, together with culturally sensitization, make it particularly suitable for use in community-based settings and public health programs aimed at improving dietary behaviors and health outcomes in Israel's diverse population. The strong correlation between the M-SPFL scale and the I-MEDAS underscores the importance of focusing on FL skills and competencies as an integral part of any intervention targeting nutritional change. The findings in this study have important implications for nutrition and public health practice in Israel; the validated M-SPFL scale constitutes a reliable tool for assessing FL, which can inform the development and evaluation of targeted interventions. This adapted measure has been shortened yet incorporates additional items, which may enable similar measures to be validated in other geographic regions. Different socio-demographic FL findings suggest that interventions should be tailored to different population segments, particularly those with lower education levels and younger adults, in order to reduce disparities and increase food literacy in different populations. Abbreviations FL: food literacy; SPFL: short perceived food literacy scale; M-SPFL: modified short perceived food literacy scale; MEDAS: Mediterranean diet adherence screener; I-MEDAS: Israeli Mediterranean diet adherence screener, CFA: confirmatory factor analysis; EFA: exploratory factor analysis. Declarations Ethical approval and consent to participate declaration: The study was approved by the Institutional Ethics Committee of Hadassah University Medical Center (HMO-0135-19, approved March 2019). All study participants provided their written consent to participate in the study. All methods were carried out in accordance with relevant guidelines and regulations. Consent for publication: Not applicable. Data Availability: Data will be made available on request. Competing interests: The authors declare that they have no competing interest. Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Authors’ Contributions: Keren L. Greenberg: Writing – original draft, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Yael Bar-Zeev: Writing – review & editing, Supervision, Methodology, Formal analysis, Conceptualization. Milka Donchin: Writing – review & editing, Supervision, Methodology, Formal analysis, Conceptualization. Donna R. Zwas: Writing – review & editing, Supervision, Methodology, Formal analysis, Conceptualization. Acknowledgments: The authors would like to thank the experts who took part in the face validity process for their valuable feedback, all the participants who filled out the pre-test and online survey for their contribution, and Dr. Amir Hefetz and Dr. Gabi Liberman for statistical assistance and input. References Vidgen HA, Gallegos D. Defining food literacy and its components. Appetite. 2014;76:50–9. https://doi.org/10.1016/j.appet.2014.01.010 . Cullen T, Hatch J, Martin W, Higgins JW, Sheppard R. Food literacy: Definition and framework for action. Can J Diet Pract Res. 2015;76(3):140–5. https://doi.org/10.3148/cjdpr-2015-010 . Krause C, Sommerhalder K, Beer-Borst S, Abel T. Just a subtle difference? Findings from a systematic review on definitions of nutrition literacy and food literacy. Health Promot Int. 2018;33(3):378–89. https://doi.org/10.1093/heapro/daw084 . Hernandez KJ, Gillis D, Kevany K, Kirk S. Towards a common understanding of food literacy: A pedagogical framework. Can Food Stud. 2021;8(4). https://doi.org/10.15353/cfs-rcea.v8i4.467 . Azevedo Perry E, Thomas H, Samra HR, Edmonstone S, Davidson L, Faulkner A, Petermann L, Manafò E, Kirkpatrick SI. Identifying attributes of food literacy: A scoping review. Public Health Nutr. 2017;20(13):2406–15. https://doi.org/10.1017/S1368980017001276 . Rosas R, Pimenta F, Leal I, Schwarzer R. FOODLIT-PRO: Conceptual and empirical development of the food literacy wheel. Int J Food Sci Nutr. 2021;72(1):99–111. https://doi.org/10.1080/09637486.2020.1762547 . Truman E, Lane D, Elliott C. Defining food literacy: A scoping review. Appetite. 2017;116:365–71. https://doi.org/10.1016/j.appet.2017.05.007 . Amouzandeh C, Fingland D, Vidgen HA. A Scoping review of the validity, reliability and conceptual alignment of food literacy measures for adults. Nutrients. 2019;11(4):801. https://doi.org/10.3390/nu11040801 . Poelman MP, Dijkstra SC, Sponselee H, Kamphuis CBM, Battjes-Fries MCE, Gillebaart M, Seidell JC. Towards the measurement of food literacy with respect to healthy eating: The development and validation of the self perceived food literacy scale among an adult sample in the Netherlands. Int J Behav Nutr Phys Act. 2018;15(1):54. https://doi.org/10.1186/s12966-018-0687-z . Boslooper-Meulenbelt K, Boonstra MD, van Vliet IMY, Gomes-Neto AW, Osté MCJ, Poelman MP, Bakker SJL, de Winter AF, Navis GJ. Food literacy is associated with adherence to a Mediterranean-style diet in kidney transplant recipients. J Ren Nutr. 2021;31(6):628–36. https://doi.org/10.1053/j.jrn.2020.12.010 . Lee Y, Kim T, Jung H. Effects of university students’ perceived food literacy on ecological eating behavior towards sustainability. Sustainability. 2022;14(9):5242. https://doi.org/https://doi.org/10.3390/su14095242 . Luque B, Villaécija J, Ramallo A, de Matos MG, Castillo-Mayén R, Cuadrado E, Tabernero C. Spanish validation of the self-perceived food literacy scale: A Five-factor model proposition. Nutrients. 2022;14(14):2902. https://doi.org/10.3390/nu14142902 . Selçuk KT, Çevik C, Baydur H, Meseri R. Validity and reliability of the Turkish version of the self-perceived food literacy scale. Prog Nutr. 2020;22:671–7. https://doi.org/10.23751/pn.v22i2.9662 . Sponselee HCS, Kroeze W, Poelman MP, Renders CM, Ball K, Steenhuis IHM. Food and health promotion literacy among employees with a low and medium level of education in the Netherlands. BMC Public Health. 2021;21(1):1273. https://doi.org/10.1186/s12889-021-11322-6 . Trieste L, Bazzani A, Amato A, Faraguna U, Turchetti G. Food literacy and food choice–a survey-based psychometric profiling of consumer behaviour. Br Food J. 2021;123(13):124–41. https://doi.org/10.1108/BFJ-09-2020-0845 . Zastrow F, Neher K, Pentner C, Hassel H. Eating an enjoyable and balanced diet–food literacy among older adults. Ernahr Umsch. 2021;68(3):53–60. Boedt T, Steenackers N, Verbeke J, Vermeulen A, De Backer C, Yiga P, Matthys C. A Mixed-method approach to develop and validate an integrated food literacy tool for personalized food literacy guidance. Front Nutr. 2022;8:760493. https://doi.org/10.3389/fnut.2021.760493 . Park D, Park YK, Park CY, Choi MK, Shin MJ. Development of a comprehensive food literacy measurement tool integrating the food system and sustainability. Nutrients. 2020;12(11):3300. https://doi.org/10.3390/nu12113300 . Paynter E, Begley A, Butcher LM, Dhaliwal SS. The validation and improvement of a food literacy behavior checklist for food literacy programs. Int J Environ Res Public Health. 2021;18(24):13282. https://doi.org/10.3390/ijerph182413282 . Rosas R, Pimenta F, Leal I, Schwarzer R. FOODLIT-tool: Development and validation of the adaptable food literacy tool towards global sustainability within food systems. Appetite. 2022;168:105658. https://doi.org/10.1016/j.appet.2021.105658 . So H, Park D, Choi MK, Kim YS, Shin MJ, Park YK. Development and validation of a food literacy assessment tool for community-dwelling elderly people. Int J Environ Res Public Health. 2021;18(9):4979. https://doi.org/10.3390/ijerph18094979 . Thompson C, Byrne R, Adams J, Vidgen HA. Development, validation and item reduction of a food literacy questionnaire (IFLQ-19) with Australian adults. Int J Behav Nutr Phys Act. 2022;19(1):113. https://doi.org/10.1186/s12966-022-01351-8 . Yoo H, Jo E, Lee H, Park S. Development of a food literacy assessment tool for healthy, joyful, and sustainable diet in South Korea. Nutrients. 2022;14(7):1507. https://doi.org/10.3390/nu14071507 . Zhang Y, Zhang Z, Xu M, Aihemaitijiang S, Ye C, Zhu W, Ma G. Development and validation of a food and nutrition literacy questionnaire for Chinese adults. Nutrients. 2022;14(9):1933. 10.3390/nu14091933 . Greenberg KL, Bar-Zeev Y, Donchin M, Karjawally M, Sneineh SA, Husseini MN, Zwas DR. Feasibility, acceptability and preliminary effectiveness of a manualized lay-led food literacy intervention for women in a community setting. Appetite. 2025;207:107885. https://doi.org/10.1016/J.APPET.2025.107885 . Begley A, Paynter E, Dhaliwal SS. Evaluation tool development for food literacy programs. Nutrients. 2018;10(11):1617. https://doi.org/10.3390/nu10111617 . Wijayaratne SP, Reid M, Westberg K, Worsley A, Mavondo F. Food literacy, healthy eating barriers and household diet. Eur J Mark. 2018;52(12):2449–77. https://doi.org/10.1108/EJM-10-2017-0760 . Gréa Krause C, Beer-Borst S, Sommerhalder K, Hayoz S, Abel T. A short food literacy questionnaire (SFLQ) for adults: Findings from a Swiss validation study. Appetite. 2018;120:275–80. 10.1016/j.appet.2017.08.039 . Samuel H, Maoz Breuer R. Food consumption habits and attitudes to the nutrition labeling program. Myers-JDC-Brookdale Institute. 2020. https://brookdale.jdc.org.il/en/publication/food-consumption-habits-and-attitudes-to-nutrition-labeling-program/ . Accessed 24 April 2025. Israel Center for Disease Control, Ministry of Health. Rav Mabat adult second national health and nutritional survey, ages 18–64, 2014–2016. Publication 383. 2019. https://www.gov.il/BlobFolder/reports/mabat-adults-2014-2016-383/en/files_publications_units_ICDC_mabat_adults_2014_2016_383_en.pdf Israel Center for Disease Control, Ministry of Health. Israel National Health Interview Survey INHIS-4, selected findings 2018–2020. 2022. https://www.gov.il/en/pages/02082022-02 . Accessed 24 April 2025. Kalter-Leibovici O, Chetrit A, Lubin F, Atamna A, Alpert G, Ziv A, Abu-Saad K, Murad H, Eilat-Adar S, Goldbourt U. Adult-onset diabetes among Arabs and Jews in Israel: A population-based study. Diabet Med. 2012;29(6):748–54. 10.1111/j.1464-5491.2011.03516.x . Lake AA, Hyland RM, Mathers JC, Rugg-Gunn AJ, Wood CE, Adamson AJ. Food shopping and preparation among the 30‐somethings: Whose job is it? (The ASH30 study). Br Food J. 2006;108(6):475–86. https://doi.org/10.1108/00070700610668441 . Reid M, Worsley A, Mavondo F. The obesogenic household: Factors influencing dietary gatekeeper satisfaction with family diet. Psychol Mark. 2015;32(5):544–57. https://doi.org/10.1002/mar.20799 . Wijayaratne S, Westberg K, Reid M, Worsley A. A qualitative study exploring the dietary gatekeeper's food literacy and barriers to healthy eating in the home environment. Health Promot J Austr. 2021;32(Suppl 2):292–300. 10.1002/hpja.398 . Guiné RPF, Florença SG, Aparício G, Cardoso AP, Ferreira M. food literacy scale: Validation through exploratory and confirmatory factor analysis in a sample of Portuguese university students. Nutrients. 2022;15(1):166. 10.3390/nu15010166 . Dernini S, Berry EM, Serra-Majem L, La Vecchia C, Capone R, Medina FX, Aranceta-Bartrina J, Belahsen R, Burlingame B, Calabrese G, Corella D, Donini LM, Lairon D, Meybeck A, Pekcan AG, Piscopo S, Yngve A, Trichopoulou A. Med Diet 4.0: The Mediterranean diet with four sustainable benefits. Public Health Nutr. 2017;20(7):1322–30. 10.1017/S1368980016003177 . Tsang S, Royse CF, Terkawi AS. Guidelines for developing, translating, and validating a questionnaire in perioperative and pain medicine. Saudi J Anaesth. 2017;11(Suppl 1):S80–9. 10.4103/sja.SJA_203_17 . Hefetz A, Liberman G. The factor analysis procedure for exploration: A short guide with examples. Cult Edu. 2017;29(3):526–62. https://doi.org/10.1080/11356405.2017.1365425 . Israeli Center Bureau of Statistics. Israel in figures: Selected data from the statistical abstract of Israel 2021. 2022. Bezeq. The state of internet in Israel Report 2022. 2022. https://media.bezeq.co.il/pdf/internetreport_2022.pdf [Hebrew] Israeli Central Bureau of Statistics. Education level of persons aged 25–66 according to the CBS education register, 2009–2022. 2025. https://www.cbs.gov.il/he/mediarelease/DocLib/2025/040/06_25_040b.pdf [Hebrew] Dewitt J, Capistrant B, Kohli N, Rosser BRS, Mitteldorf D, Merengwa E, West W. Addressing participant validity in a small internet health survey (The Restore Study): Protocol and recommendations for survey response validation. JMIR Res Protoc. 2018;7(4):e96. 10.2196/resprot.7655 . Hauser DJ, Ellsworth PC, Gonzalez R. Are manipulation checks necessary? Front Psychol. 2018;9:998. 10.3389/fpsyg.2018.00998 . Heffner JL, Watson NL, Dahne J, Croghan I, Kelly MM, McClure JB, Bars M, Thrul J, Meier E. Recognizing and preventing participant deception in online nicotine and tobacco research studies: Suggested tactics and a call to action. Nicotine Tob Res. 2021;23(10):1810–2. 10.1093/ntr/ntab077 . Abu-Saad K, Endevelt R, Goldsmith R, Shimony T, Nitsan L, Shahar DR, Keinan-Boker L, Ziv A, Kalter-Leibovici O. Adaptation and predictive utility of a Mediterranean diet screener score. Clin Nutr. 2019;38(6):2928–35. 10.1016/j.clnu.2018.12.034 . Schröder H, Fitó M, Estruch R, Martínez-González MA, Corella D, Salas-Salvadó J, Lamuela-Raventós R, Ros E, Salaverría I, Fiol M, Lapetra J, Vinyoles E, Gómez-Gracia E, Lahoz C, Serra-Majem L, Pintó X, Ruiz-Gutierrez V, Covas MI. A short screener is valid for assessing Mediterranean diet adherence among older Spanish men and women. J Nutr. 2011;141(6):1140–5. 10.3945/jn.110.135566 . Brown TA. Confirmatory factor analysis for applied research, second edition. Guilford Publications. 2015. Osborne JW. Best practice in exploratory factor analysis. Createspace publishing. 2014. Marsh HW, Hau KT, Wen Z. In Search of golden rules: Comment on hypothesis-testing approaches to setting cutoff values for fit indexes and dangers in overgeneralizing Hu and Bentler’s (1999) findings. Struct Equ Model. 2004;11(3), 320–341. https://doi.org/10.1207/s15328007sem1103_2 Samuels P. Scale formation: Scale reliability analysis and exploratory factor analysis. In Researching and Analysing Business: Research Methods in Practice Taylor and Francis. 2023 (pp. 283–294). https://doi.org/10.4324/9781003107774-22 Henseler J, Ringle CM, Sarstedt M. A new criterion for assessing discriminant validity in variance-based structural equation modeling. J Acad Mark Sci. 2015;43:115–35. https://doi.org/10.1007/s11747-014-0403-8 . Raykov T. Estimation of composite reliability for congeneric measures. 1997;21(2),173–84. https://doi.org/10.1177/01466216970212006 Dunn TJ, Baguley T, Brunsden V. From alpha to omega: A practical solution to the pervasive problem of internal consistency estimation. 2014;105(3):399–412. https://doi.org/10.1111/bjop.12046 Kolpatzik K. Ernährungskompetenz in Deutschland. In Gesundheitskompetenz. Heidelberg: Springer Berlin Heidelberg; 2022. pp. 1–11. Desjardins E, Azevedo E, Davidson L, Samra R, MacDonald A, Dunbar J, Thomas H, Munoz MA, King B, Maxwell T, Wong-McGraw P, Shukla R, Traynor M. Making something out of nothing: Food literacy among youth, young pregnant women and young parents who are at risk for poor health, a locally driven collaborative project of Public Health Ontario. 2013. Palumbo R, Adinolfi P, Annarumma C, Catinello G, Tonelli M, Troiano E, Vezzosi S, Manna R. Unravelling the food literacy puzzle: Evidence from Italy. Food Policy. 2019;83:104–15. https://doi.org/10.1016/J.FOODPOL.2018.12.004 . Shafran I, Benyamini Y, Keinan-Boker L, Gerber Y. Self-rated health and mortality among older adults in Israel: A comparison between Jewish and Arab Populations. J Clin Med. 2024;13(22):6978. 10.3390/jcm13226978 . Rozani V. Ethnic differences in socioeconomic and health determinants related to self-rated health status: A study on community-dwelling Israeli Jews and Arabs in old age. Int J Environ Res Public Health. 2022;19(20):13660. 10.3390/ijerph192013660 . Baron-Epel O, Kaplan G, Haviv-Messika A, Tarabeia J, Green MS, Kaluski DN. Self-reported health as a cultural health determinant in Arab and Jewish Israelis MABAT–National Health and Nutrition Survey 1999–2001. Soc Sci Med. 2005;61(6):1256–66. https://doi.org/10.1016/J.SOCSCIMED.2005.01.022 . Additional Declarations No competing interests reported. Supplementary Files Supplement1.docx Supplement2.docx Supplement3.docx Supplement4.docx Cite Share Download PDF Status: Published Journal Publication published 04 Dec, 2025 Read the published version in BMC Public Health → Version 1 posted Editorial decision: Revision requested 15 Jul, 2025 Reviews received at journal 03 Jul, 2025 Reviews received at journal 17 Jun, 2025 Reviewers agreed at journal 08 Jun, 2025 Reviewers agreed at journal 05 Jun, 2025 Reviewers invited by journal 05 Jun, 2025 Editor invited by journal 15 May, 2025 Editor assigned by journal 14 May, 2025 Submission checks completed at journal 14 May, 2025 First submitted to journal 12 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6644848","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":467831422,"identity":"f6e57a7a-1ea8-40f4-932b-62e27a61ef8a","order_by":0,"name":"Keren L Greenberg","email":"","orcid":"","institution":"Braun School of Public Health and Community Medicine, Faculty of Medicine, Hebrew University of Jerusalem and Hadassah University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Keren","middleName":"L","lastName":"Greenberg","suffix":""},{"id":467831423,"identity":"a4a6c092-e060-4079-be26-1753c6e0617c","order_by":1,"name":"Donna R Zwas","email":"","orcid":"","institution":"The Linda Joy Pollin Cardiovascular Wellness Center for Women, Hadassah University Medical Center, Israel","correspondingAuthor":false,"prefix":"","firstName":"Donna","middleName":"R","lastName":"Zwas","suffix":""},{"id":467831424,"identity":"1aba0d72-1cbd-4953-9480-a82d86f7d69d","order_by":2,"name":"Milka Donchin","email":"","orcid":"","institution":"Braun School of Public Health and Community Medicine, Faculty of Medicine, Hebrew University of Jerusalem and Hadassah University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Milka","middleName":"","lastName":"Donchin","suffix":""},{"id":467831425,"identity":"294e1bf2-c2c7-4359-80ec-9dededa28fe9","order_by":3,"name":"Yael Bar-Zeev","email":"data:image/png;base64,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","orcid":"","institution":"Braun School of Public Health and Community Medicine, Faculty of Medicine, Hebrew University of Jerusalem and Hadassah University Medical Center","correspondingAuthor":true,"prefix":"","firstName":"Yael","middleName":"","lastName":"Bar-Zeev","suffix":""}],"badges":[],"createdAt":"2025-05-12 09:08:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6644848/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6644848/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12889-025-25597-6","type":"published","date":"2025-12-04T15:58:04+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":84448123,"identity":"e9389d90-4165-48f7-9532-428cc220f068","added_by":"auto","created_at":"2025-06-12 06:13:19","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":60436,"visible":true,"origin":"","legend":"\u003cp\u003eOnline Survey Flowchart\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6644848/v1/c687fb055347c6772bdda1cd.jpg"},{"id":84448127,"identity":"b6864345-27a8-45c4-88e0-5af1469b83d1","added_by":"auto","created_at":"2025-06-12 06:13:19","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":616754,"visible":true,"origin":"","legend":"\u003cp\u003eConfirmatory factor analysis (CFA) results for the final M-SPFL model.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6644848/v1/10d6631e0aab4b3250fdd37f.jpg"},{"id":84448483,"identity":"6a2f9822-ef1f-4260-a9af-51285c245555","added_by":"auto","created_at":"2025-06-12 06:21:19","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":253772,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e3a.\u003c/strong\u003e FL level distribution %(n), N=2120\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3b.\u003c/strong\u003e FL level distribution by sector %(n), N=2120\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6644848/v1/17b29a1593273fccf722ac87.jpg"},{"id":97724648,"identity":"fe2cc8dd-1aef-4851-aaa0-680774f99d30","added_by":"auto","created_at":"2025-12-08 16:13:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2052478,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6644848/v1/3490ecc0-860c-487e-9bf5-da5f0eb369cb.pdf"},{"id":84449183,"identity":"aeb5e9c7-e5bb-4748-905c-65809c05a30b","added_by":"auto","created_at":"2025-06-12 06:29:19","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":35317,"visible":true,"origin":"","legend":"","description":"","filename":"Supplement1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6644848/v1/ef813034783b7e717259deee.docx"},{"id":84448485,"identity":"4f50116d-d796-420d-90e1-c4dfb03f8def","added_by":"auto","created_at":"2025-06-12 06:21:19","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":18167,"visible":true,"origin":"","legend":"","description":"","filename":"Supplement2.docx","url":"https://assets-eu.researchsquare.com/files/rs-6644848/v1/edf39814077299e3512fd73d.docx"},{"id":84448489,"identity":"f8463fc0-d2f7-4195-904a-928504ced655","added_by":"auto","created_at":"2025-06-12 06:21:20","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":18503,"visible":true,"origin":"","legend":"","description":"","filename":"Supplement3.docx","url":"https://assets-eu.researchsquare.com/files/rs-6644848/v1/5cbfdec22a869e928b1ac688.docx"},{"id":84449184,"identity":"9a7b0476-b688-4007-8f39-f305f915cd70","added_by":"auto","created_at":"2025-06-12 06:29:19","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":32686,"visible":true,"origin":"","legend":"","description":"","filename":"Supplement4.docx","url":"https://assets-eu.researchsquare.com/files/rs-6644848/v1/66eb969e21bc2a6af7cd86d6.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Validation and adaptation of the short-perceived food literacy scale (SPFL) among Israeli women","fulltext":[{"header":"1. Background","content":"\u003cp\u003eThe concept of food literacy (FL) has emerged in recent years to address the multiple skills, competencies, and external factors that affect an individual's ability to make healthy nutritional choices. One widely accepted definition of FL, offered by Vidgen and Gallegos [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], and often cited when referring to FL in the literature, is: \"\u003cem\u003ethe scaffolding that empowers individuals, households, communities and nations, to protect diet quality through change and strengthen dietary resilience over time. It is composed of a collection of inter-related knowledge, skills and behaviours required to plan, manage, select, prepare and eat food to meet needs and determine intake\u003c/em\u003e\" [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The authors identified 4 dimensions of FL, focused on individual skills and abilities: (1) planning and managing, (2) selecting, (3) preparing, and (4) eating [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Cullen et al. [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] added that FL should also consider environmental, social, economic, cultural, and political components [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], and in the following years many others have added additional definitions to the FL concept [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Multiple frameworks have been developed in the last decade, emphasizing different constructs [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Despite a variety of definition and frameworks, common themes appear in many FL related papers. In 2017, Truman et al. examined 38 novel FL definitions and found 6 common themes integrated in these conceptualizations: knowledge, emotions, skills/behaviours, health/food choices, culture and the broader food system [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe variety of definitions, frameworks and models for FL has resulted in a variety of measurement tools [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], while there is currently no consensus on one specific tool. A recent scoping review from 2019 that aimed to identify existing tools that measure FL in adults [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] found a total of 12 different tools. Poelman's short perceived FL scale (SPFL)[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], based on the Vidgen and Gallegos model [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], is one of five scales recommended by this review [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The SPFL has been used multiple times and validated in various languages [\u003cspan additionalcitationids=\"CR11 CR12 CR13 CR14 CR15\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Since the 2019 review, additional studies describing novel FL measuring instruments for adults have been published [\u003cspan additionalcitationids=\"CR18 CR19 CR20 CR21 CR22 CR23\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], most of which do not capture all of the Vidgen and Gallegos domains and have been scarcely used. The SPFL consists of 29 items that cover eight domains of FL: food preparation skills, resilience and resistance, healthy snacking, social and conscious eating, examining food labels, daily food planning, healthy budgeting and healthy food stockpiling [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. A higher mean SPFL score was correlated with healthier food consumption [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], and specifically with adherence to the Mediterranean diet [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. However, the SPFL does not include several items that may be important and are included in other tools, such as those that reflect shopping preparations [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], portion size [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], intentionally planning/consuming healthy meals [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], or altering a recipe to make it healthier [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Additionally, culture is included in several tools [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] while it is absent in the SPFL. Moreover, the SPFL does not include certain aspects of FL that have emerged in recent years, such as sustainability conscientiousness [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], whether referring to food waste/food related waste, methods of agricultural and livestock production, transportation and distribution, or awareness of food decisions impact on environment and climate sustainability.\u003c/p\u003e \u003cp\u003eIn Israel, the need to target and measure FL skills and behaviours is apparent from studies presenting prevalence of factors associated with reduced FL, including low rates of reported nutritional label examination [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] and eating family meals [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], high rates of reported purchase of processed food [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], eating out of boredom and eating while watching TV or working [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. These findings, in addition to the high prevalence of risk factors among Israelis [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], emphasize the importance in utilizing a validated culturally-adapted FL measuring tool to map, monitor and intervene in this field.\u003c/p\u003e \u003cp\u003eAn adapted (yet not validated) version of the SPFL has been previously used in Israel in Hebrew and Arabic, evaluating a FL intervention in the community [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The results indicated an increase in FL levels post intervention and correlation between FL and healthy eating habits. However, the study also found that participants perceived the survey as too long to fill out, which affected post intervention survey completion rates.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003eThis study aims to validate and shorten a FL assessment scale based on the SPFL survey among Hebrew and Arabic speaking women in Israel. The study focuses on women as they are more likely to be the household dietary gatekeepers [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] (i.e., do the majority of grocery shopping, meal planning and cooking). Dietary gatekeepers hold a major influence on their family's diet, and act as a significant determinant of the food consumed in their home [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], and several FL tool validation studies included mainly women [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. For the validation of the FL tool, this study used the SPFL survey with an additional 6 questions reflecting aspects absent in the original SPFL (Supplement 1), including items concerning culture, shopping preparations, portion size, intentionally consuming healthy meals, altering a recipe to make it healthier and food waste. This study's secondary objective is to explore associations between socio-demographic variables and FL levels among Hebrew and Arabic-speaking Israeli women.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Procedure\u003c/h2\u003e \u003cp\u003eThe validation process was comprised of three steps: the first step included assessment of face validity and a survey pretest in target communities. The second step included an online survey to test content validity in a large sample, by conducting both a confirmatory factor analysis (CFA) on the original 29 SPFL items in order to confirm the fit of the model and its domains in the Israeli sample, and an exploratory factor analysis (EFA) to examine the integration of the 6 additional items in the model. This stage also included reduction of questions that do not add to the validity of the tool in order to shorten the survey. The final factor model was then confirmed using a second CFA. Reliability tests were performed to examine the internal consistency of the final scale and within each of its domains. The third step included convergent validity, testing the correlations between the SPFL scale and the Israeli Mediterranean Diet Adherence Scale (I-MEDAS) scale. The Mediterranean diet is the recommended diet in Israel, associated with decreased obesity, diabetes, cardiovascular disease, dementia and cancer [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In addition to the validation process mentioned above, associations between socio-demographic variables and FL levels were examined to elucidate the construct and its predictors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Step 1: Face Validity and Pretest\u003c/h2\u003e \u003cp\u003ePrior to face validity, the 29-item SPFL survey was translated into Hebrew and Arabic using two forward translators, one of which is a certified translator and the other a bilingual researcher, and a third translator independently checked the forward translation against the original questionnaire and resolved any discrepancies. An additional six questions were added based on validated measures and adapted to the target population (Supplement 1). This adapted extended SPFL (35-items) underwent face validation and pre-testing: the face validity process included sending the questionnaire to five nutrition/health literacy experts (including two Arabic speakers), who replied with feedback on the scale, including its cultural appropriateness, and its fitness/representation of the FL framework. The experts received the FL definition and the Vidgen and Gallegos model the SPFL is based on in order to better gauge the fitness of the scale. Next, the questionnaire was pretested both in Hebrew and in Arabic among 24 women from different backgrounds and education levels through existing community groups, to test readability and understandability. After each of these steps, the survey was amended as necessary. The amendments included slight rephrasing and text changes, though no major alteration to the original text were necessary.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Step 2: Content validation\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1. Design\u003c/h2\u003e \u003cp\u003eAn online cross-sectional survey conducted between March-July 2024.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2. Study Sample and Recruitment\u003c/h2\u003e \u003cp\u003eThe sample size was calculated following guidelines for the respondent-to-item ratio, ranging from 5:1 to 30:1 [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] (i.e., five to thirty respondents per every item on the scale to be validated), with the accepted \"rule of thumb\" between 5:1 to 10:1 ratio [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. In this study we used a 10:1 ratio, resulting in a sample size of 350 in each population (Arabic and Hebrew speaking) for the validation of the 35-item survey. To assure this sample size is large enough to detect the correlation between FL and adherence to the Mediterranean diet, a sample size was calculated using the \"PS Power and Sample Size\" calculator, based on data from a study describing correlation between the measures (SD\u003csub\u003ex\u003c/sub\u003e = 0.44, SD\u003csub\u003ey\u003c/sub\u003e = 1.7, β\u0026thinsp;=\u0026thinsp;1.51) [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], using 5% significance level, and 90% power, and resulting in a sample size of 60, well within the calculated sample size described above.\u003c/p\u003e \u003cp\u003e Participants were recruited through Facebook advertisements in Hebrew and Arabic that were promoted to target adult women (over the age of 25, as younger women are less likely to be the dietary gatekeepers) from different areas in Israel (90% of Israelis over the age of 20 use the internet [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], 75% are active on Facebook [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]), with the incentive of enrolment in a prize-winning raffle worth 300 NIS (~\u003cspan\u003e$\u003c/span\u003e80 USD). After surpassing the initial sample size, preliminary data analysis revealed most respondents to be of higher education levels. Therefore, additional sampling took place targeting women with lower education levels aiming to receive a more representative sample and to enable sub-analyses of this group (in Israel 40% of women have academic degrees, 55% have high school diplomas or lesser education) [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRespondents who were not female, were under the age of 25, or who were not Israeli citizens or residents, were navigated out of the online survey before moving forward. An additional open question at the end of the survey inquired about current place of residence; respondents who do not reside in Israel at the time of survey response were not included in the final sample. As this survey utilized social media recruitment, a predefined protocol to eliminate duplicate and invalid survey responses was employed [\u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. This included manual screening for internal inconsistencies, low differentiation of responses, high level of item unresponsiveness, and exclusion of respondents who answered an attention check question incorrectly.\u003c/p\u003e \u003cp\u003eOnly respondents who completed 100% of the extended SPFL and I-MEDAS scales and who correctly answered the attention check question were included in the final sample. For respondents with multiple entries, only the first or complete entry were included.\u003c/p\u003e \u003cp\u003eThe final sample included n\u0026thinsp;=\u0026thinsp;2129 respondents (653 Arabic speakers, 1476 Hebrew speakers), see flowchart in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3. Study Measures\u003c/h2\u003e \u003cp\u003eThe online survey contained the translated culturally-adapted extended SPFL scale (after face validation and pre-testing in step 1), the I-MEDAS scale [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], and socio-demographic questions.\u003c/p\u003e \u003cp\u003eThe extended SPFL survey consisted of 35 questions rated on a 5-point Likert scale (\"never\" to \"always\" / \"not at all\" to \"to a very large extent\", calculated as an average score) (Supplement 1). The I-MEDAS, adapted from the Mediterranean diet adherence screener (MEDAS) [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], consists of 17 questions and is scored based on a pre-specified scoring method (range 0\u0026ndash;17). The I-MEDAS has been validated in both Hebrew and in Arabic, with improved scores associated with reduced mortality risk [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Additionally, data regarding socio-demographic variables were collected, including year of birth, country of birth, education level, marital status, number of children and place of residence.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.3.4. Data cleaning\u003c/h2\u003e \u003cp\u003eThe response distribution for each of the 35 questions was examined, and questions where 60% or more of the responses were in the lower or higher ends of the Likert scale (i.e. answers 1 or 5) were dropped, as they do not contribute to the variance of the scale [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. This procedure identified one question, question 29 of the original SPFL scale, with 61.6% of respondents answering '5' on the Likert scale. The question was subsequently dropped from the final scale analysis, which included 34 items. Additionally, individual responses were examined in order to identify respondents who supplied the same response on the Likert scale across 30 or more of the 35 SPFL items, as these respondents do not add the variance of the sample and are suspected to be unauthentic [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Nine such respondents were identified in this process and removed from the final analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.3.5. Data Analysis\u003c/h2\u003e \u003cdiv id=\"Sec11\" class=\"Section4\"\u003e \u003ch2\u003e2.3.5.1. Descriptive statistics\u003c/h2\u003e \u003cp\u003eDescriptive statistics, including proportions for categorical variables, and mean and standard deviation (SD) for continuous variables, were used to assess sociodemographic data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section4\"\u003e \u003ch2\u003e2.3.5.2. Confirmatory (CFA) and Exploratory factor analysis (EFA)\u003c/h2\u003e \u003cp\u003eIn order to perform reliable analyses, we divided the sample into two randomly selected adequately-powered subsamples for training (exploratory) and testing (confirmatory). This division was conducted to test and confirm the fit of the suggested structure on another random set of data [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe CFA was first performed to test the original 29-item SPFL division into the eight domains presented by Poelman et al.[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. This test aimed to determine the extent to which our empirical data matched with the original instrument. The test was conducted on the training sample, and then confirmed on the testing sample.\u003c/p\u003e \u003cp\u003eThe CFA model fit was determined using the comparative fit index (CFI, levels over 0.9 indicate a fair model fit, level over 0.95 indicate a good model fit [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], root mean square error of approximation (RMSEA, levels under 0.06 indicate a good fit [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] and standardized root mean square residuals (SRMR, levels under 0.08 indicate a good fit [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNext, EFA was conducted to test the integration of the six new items in the SPFL tool, and to explore whether the tool can be shortened by eliminating items that do not fit or contribute to the new-found domains. To that extent, the EFA was performed initially without rotations and items with poor loading (λ\u0026thinsp;\u0026lt;\u0026thinsp;0.40) or multiple loading[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] were excluded during the EFA analysis. Next, the EFA was performed with rotation in order to place the remaining items in the identified domains. In this analysis principal axis factoring (PAF) was used as the extraction method, with the Promax (oblique) rotation method [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The Kaiser-Meyer-Olkin coefficient (KMO, considered reasonable if over 0.6, and good if over 0.8) and Bartlett\u0026rsquo;s Test of Sphericity (considered good if significance is under 0.05) determined data adequacy for EFA [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFinally, to assess how distinct each new domain is compared to the others, the average variance extracted (AVE) was calculated and compared to the maximum shared variance (MSV), which is the square of the highest correlation found between each domain and every other domain. The common threshold for AVE is greater or equal to 0.50, but more importantly the AVE is expected to be greater than the MSV, which indicates a discriminant validity for all domains [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section4\"\u003e \u003ch2\u003e2.3.5.3. Reliability\u003c/h2\u003e \u003cp\u003eAfter conducting both CFA for the original SPFL and EFA for the extended SPFL (resulting in a shorter modified tool), internal consistency was determined for both scales and for each domain within the scale using composite reliability (CR) as a measure of percent shared variance across domain items (squared sum of factor loadings over the sum of the observed item variance plus the former [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], which resembles McDonald\u0026rsquo;s Omega [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. A CR value greater or equal to 0.7 is considered an indicator of good internal consistency.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section4\"\u003e \u003ch2\u003e2.3.5.4. Hebrew/Arabic confirmation\u003c/h2\u003e \u003cp\u003eIn order to confirm whether the modified SPFL (M-SPFL) is a good fit for both the Hebrew and Arabic speaking populations, a CFA of the final tool was performed separately for each sub-population. Additionally, a multi-group comparison was conducted to measure invariance across the six domains between Hebrew and Arabic speakers.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Step 3: Convergent validity\u003c/h2\u003e \u003cp\u003eThis step included testing the convergent validity of the M-SPFL scale by examining the Spearman correlations between the M-SPFL and the SPFL scores, and the M-SPFL and the I-MEDAS scores on the data collected as part of step 2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Associations between socio-demographic variables and FL levels\u003c/h2\u003e \u003cp\u003eAfter validation of the tool, the M-SPFL was treated as a continuous scale (scored by its mean) for further analyses. Additionally, the M-SPFL was divided into 3 levels, based on the Likert scale (negative answers (1\u0026ndash;2), neutral (3) and positive answers (4\u0026ndash;5)): inadequate (1.00 to 2.49 points), problematic (2.50 to 3.49 points), sufficient (3.50 to 5 points). This division is similar to that presented by Kolpatzik and Zaunbreche [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Both the M-SPFL mean score and category proportions were used to assess FL level in the study sample. ANOVA was used to test differences in the mean M-SPFL and I-MEDAS scores between sectors, and the chi-square test was used to test differences in category proportion between sectors.\u003c/p\u003e \u003cp\u003ePearson and Spearman tests were conducted to examine associations between socio-demographic continuous and ordinal variables with FL levels, as well as ANOVA tests for categorical variables. The general linear model was performed to test the associations in a single model and included all variables that were suggested to be associated with M-SPFL (p\u0026thinsp;\u0026lt;\u0026thinsp;.05) in the bivariate analyses. The same model was used a second time to examine these associations in each sector separately. Additionally, a multinomial regression was conducted examining associations between the variables and M-SPFL level as a categorial variable.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Online survey demographics\u003c/h2\u003e \u003cp\u003eThe final sample of the online survey included n\u0026thinsp;=\u0026thinsp;2129 women, with a mean age of 44\u0026thinsp;\u0026plusmn;\u0026thinsp;11.8. Of the respondents, 72.3% (n\u0026thinsp;=\u0026thinsp;1538) held an academic degree, 72.4% (n\u0026thinsp;=\u0026thinsp;1539) were married with a mean number of children of 2.19\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6, 68.2% (n\u0026thinsp;=\u0026thinsp;1445) were Jewish and 26.3% (n\u0026thinsp;=\u0026thinsp;558) were Muslim (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of the online survey respondents by demographic characteristics (n\u0026thinsp;=\u0026thinsp;2129)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;2129)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHebrew\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;1476)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArabic\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;653)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e (mean, SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.2 (\u0026plusmn;\u0026thinsp;11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.3 (\u0026plusmn;\u0026thinsp;12.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41.6 (\u0026plusmn;\u0026thinsp;9.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of Children\u003c/b\u003e (mean, SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.2 (\u0026plusmn;\u0026thinsp;1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.8 (\u0026plusmn;\u0026thinsp;1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.9 (\u0026plusmn;\u0026thinsp;1.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation Level\u003c/b\u003e n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo certificate/ high school certificate/matriculations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e331 (15.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e192 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e139 (21.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVocational degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e225 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e142 (9.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e113 (17.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcademic degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1538 (72.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1141 (77.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e397 (60.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1539 (72.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e979 (66.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e560 (86.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced/ Widowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e213 (10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e166 (11.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47 (7.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e371 (17.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e329 (22.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42 (6.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReligion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJewish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1445 (68.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1445 (97.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMuslim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e558 (26.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (0.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e550 (85.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChristian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91 (4.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85 (13.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDruze\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (0.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (0.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (1.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*'Other' religion responses include: Circassian, Atheist, no religion, refuse to answer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Content validation\u003c/h2\u003e \u003cp\u003eAs mentioned previously, for the validation analyses there were 9 respondents who did not add to the tool variance and therefore they were not included in subsequent analysis (final sample N\u0026thinsp;=\u0026thinsp;2120).\u003c/p\u003e \u003cp\u003eCFA analysis testing the original 29-item SPFL, initially performed in the training subsample (N\u003csub\u003etraining\u003c/sub\u003e=1,060), indicated that the model has an acceptable level of fit: CFI\u0026thinsp;=\u0026thinsp;.930; RMSEA\u0026thinsp;=\u0026thinsp;.046, 95%CI [.043,.049]; SRMR\u0026thinsp;=\u0026thinsp;.045. These results were consistent for the testing subsample (N\u003csub\u003etesting\u003c/sub\u003e=1,060): CFI\u0026thinsp;=\u0026thinsp;.943; RMSEA\u0026thinsp;=\u0026thinsp;.040, 95%CI [.037,.044]; SRMR\u0026thinsp;=\u0026thinsp;.050.\u003c/p\u003e \u003cp\u003eThe CFA showed similar results indicating acceptable level of fit for both the Hebrew and Arabic speakers\u0026rsquo; subsamples; N\u003csub\u003etraining\u003c/sub\u003e=1,060: N\u003csub\u003eHebrew\u003c/sub\u003e=737: CFI\u0026thinsp;=\u0026thinsp;.946, RMSEA\u0026thinsp;=\u0026thinsp;.039, 95%CI [.035,.043]; SRMR\u0026thinsp;=\u0026thinsp;.041; N\u003csub\u003eArabic\u003c/sub\u003e=323: CFI\u0026thinsp;=\u0026thinsp;.929; RMSEA\u0026thinsp;=\u0026thinsp;.046, 95%CI [.039,.052]; SRMR\u0026thinsp;=\u0026thinsp;.058. These results remained consistent in the testing sub-sample, N\u003csub\u003etesting\u003c/sub\u003e=1,060: N\u003csub\u003eHebrew\u003c/sub\u003e=736: CFI\u0026thinsp;=\u0026thinsp;.932; RMSEA\u0026thinsp;=\u0026thinsp;.046, 95%CI [.042,.050]; SRMR\u0026thinsp;=\u0026thinsp;.057; N\u003csub\u003eArabic\u003c/sub\u003e=324: CFI\u0026thinsp;=\u0026thinsp;.930; RMSEA\u0026thinsp;=\u0026thinsp;.044, 95%CI [.037,.050]; SRMR\u0026thinsp;=\u0026thinsp;.062\u003c/p\u003e \u003cp\u003eEFA was performed on the training sub-sample, and confirmed on the testing sub-sample. The KMO coefficient was 0.88 and Bartlett's test was statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), confirming suitable data adequacy for EFA.\u003c/p\u003e \u003cp\u003eThe EFA, performed on the expanded 34-item SPFL, had an initial cumulative loading percent of 46.4% across 8 factors. In this EFA, nine items had a loading factor below 0.4, and were therefore removed. The EFA was performed again for the 25 remaining items, and while the cumulative loading percent went up to 50.2% (across six factors), 2 items had a loading factor below 0.4 and were removed. The final EFA was then performed for the 23 remaining items, the cumulative loading percent went up to 52.2% (across six factors), and all items had sufficient loading factors. Next, the EFA was performed using Promax (oblique) rotation, placing the 23 items into the six factors. The item division matched the original 8 SPFL domains, apart from one domain (social and conscious eating) that was fully omitted in the EFA process, and two domains that appear separately in the original SPFL but were combined in this analysis (examining food labels and daily food planning). Factor correlation matrix found that the factors were not highly correlated with each other (the highest correlation being 0.6), confirming they represent six separate domains. Four of the six new questions remained and were appropriately integrated in the domains. The remaining six domain themes are: food preparation skills (4 items), resilience and resistance (5 items), healthy snack styles (4 items), examining food labels and daily food planning (6 items), healthy budgeting (2 items), healthy food stockpiling (2 items). The final 23-item M-SPFL appears in Supplement 2.\u003c/p\u003e \u003cp\u003eCFA showed an acceptable level of fit for these six domains (N\u003csub\u003etesting\u003c/sub\u003e=1060): CFI\u0026thinsp;=\u0026thinsp;.946; RMSEA\u0026thinsp;=\u0026thinsp;.050, 95%CI [.046,.054]; SRMR\u0026thinsp;=\u0026thinsp;.044 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the AVE and MSV for each domain. Although the AVE values for four domains were below the common threshold for AVE (AVE\u0026thinsp;\u0026ge;\u0026thinsp;.50), these values are higher than their respective MSV values, which indicated a discriminant validity for these domains, i.e., the distinguishability of the six extracted domains from one another.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe rectangles in this figure indicate the 23 items of the scale (numbered according to the original scale), and the six directly connected ellipses represent the six factors. The lines between the factors and the items represent the causal effects, and each shows the standardized factor loadings of each item for its \"correlated factor\". The numbers to the left of the items are the item standardized residual variance. The errors between the factors represent covariance. Domain 1\u0026thinsp;=\u0026thinsp;Food preparation skills, Domain 2\u0026thinsp;=\u0026thinsp;Resilience and resistance, Domain 3\u0026thinsp;=\u0026thinsp;Healthy snack styles, Domain 4\u0026thinsp;=\u0026thinsp;Examining food labels and daily food planning, Domain 5\u0026thinsp;=\u0026thinsp;Healthy budgeting, Domain 6\u0026thinsp;=\u0026thinsp;Healthy food stockpiling\u003c/p\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1. Reliability\u003c/h2\u003e \u003cp\u003eThe internal consistency for both the original SPFL (CR\u0026thinsp;=\u0026thinsp;0.85) and the 23-item M-SPFL (CR\u0026thinsp;=\u0026thinsp;0.89) were high, and each of the six new domains showed good reliability as well with a CR of over 0.7 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eM-SPFL domain characteristics (n\u0026thinsp;=\u0026thinsp;2120)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDomain\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of Items\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCR.\u003c/p\u003e \u003cp\u003e[95%CI]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAVE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMSV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFood preparation skills\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.780\u003c/p\u003e \u003cp\u003e[.752,.803]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResilience and resistance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.760\u003c/p\u003e \u003cp\u003e[.734,.783]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealthy snack styles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.756\u003c/p\u003e \u003cp\u003e[.723,.782]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExamining food labels and daily food planning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.806\u003c/p\u003e \u003cp\u003e[.775,.830]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealthy budgeting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.839\u003c/p\u003e \u003cp\u003e[.810,.863]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealthy food stockpiling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.824\u003c/p\u003e \u003cp\u003e[.785,.852]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eCR-composite reliability; AVE-average variance extracted; MSV-maximum shared variance, Domain range: 1\u0026ndash;5.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2. Hebrew/Arabic confirmation\u003c/h2\u003e \u003cp\u003eCFA results show that both Hebrew and Arabic subpopulations show an acceptable level of fit for the M-SPFL model, (N\u003csub\u003etesting\u003c/sub\u003e=1060): (N\u003csub\u003eHebrew\u003c/sub\u003e=736): CFI\u0026thinsp;=\u0026thinsp;.938; RMSEA\u0026thinsp;=\u0026thinsp;.055; SRMR\u0026thinsp;=\u0026thinsp;.049; and (N\u003csub\u003eArabic\u003c/sub\u003e=324): CFI\u0026thinsp;=\u0026thinsp;.905; RMSEA\u0026thinsp;=\u0026thinsp;.065; SRMR\u0026thinsp;=\u0026thinsp;0.06. The multi-group comparison of the six domains showed that differences between the groups are only at scale level (strong-scalar invariance) but not in the factor loadings (weak-metric invariance), i.e., both populations have similar factor loadings, that is, they answered in the same pattern, but differ in their measured M-SPFL level in each domain. Thus, the division into the six construct domains represents the two groups similarly (Supplement 3).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Convergent validity\u003c/h2\u003e \u003cp\u003eThe mean M-SPFL score in this sample was 3.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58 (3.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57 for Hebrew speakers, 3.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61 for Arabic speakers, p\u0026thinsp;=\u0026thinsp;.232), and the mean I-MEDAS score was 9.01\u0026thinsp;\u0026plusmn;\u0026thinsp;2.16 (9.15\u0026thinsp;\u0026plusmn;\u0026thinsp;2.22 for Hebrew speakers, 8.69\u0026thinsp;\u0026plusmn;\u0026thinsp;2.00 for Arabic speakers, p\u0026thinsp;\u0026lt;\u0026thinsp;.001).\u003c/p\u003e \u003cp\u003ePearson correlations analyses show the M-SPFL and SPFL were highly correlated (r\u0026thinsp;=\u0026thinsp;.957, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Additionally, there was a positive correlation between the M-SPFL and the I-MEDAS (r\u0026thinsp;=\u0026thinsp;.52, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), similar to the correlation found between the original 29-item SPFL and the I-MEDAS (r\u0026thinsp;=\u0026thinsp;.51, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). These results remained the same when testing for each sector separately.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Associations between socio-demographic variables and FL levels\u003c/h2\u003e \u003cp\u003eBivariate analyses found that age, education, marital status and number of children were associated with the M-SPFL score (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all).\u003c/p\u003e \u003cp\u003eThe general linear model (using univariate ANOVA) including all the above socio-demographic variables in addition to sector (Table\u0026nbsp;3), mostly reinforced these results with a few exceptions - Hebrew speakers had lower M-SPFL scores (B\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.074, 95% CI [-.013-(-).016], p\u0026thinsp;=\u0026thinsp;.013) compared to Arabic speakers. Having an academic or vocational degree compared to matriculations or less (B\u0026thinsp;=\u0026thinsp;.0152, 95% CI [.083-.221], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; B\u0026thinsp;=\u0026thinsp;.164, 95% CI [.071-.257], p\u0026thinsp;\u0026lt;\u0026thinsp;.001, respectively), being married (B\u0026thinsp;=\u0026thinsp;.088, 95% CI [.010-.166], p\u0026thinsp;=\u0026thinsp;.026) compared to single, and age (B\u0026thinsp;=\u0026thinsp;.010, 95% CI [.007-.012], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were positively correlated with higher M-SPFL scores.\u003c/p\u003e \u003cp\u003eExploring the associations in each sector separately revealed similar results: among Hebrew speakers, being married (compared to single), having an academic or vocational degree (compared to matriculations or less) and age were correlated with increased M-SPFL scores. Among Arabic speakers having an academic degree (compared to matriculations or less) and increased age were correlated with increased M-SPFL scores (p\u0026thinsp;\u0026lt;\u0026thinsp;.001) (Supplement 4).\u003c/p\u003e \u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003eAssociations between M-SPFL and socio-demographic variables, general linear model results\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean M-SFPL (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHebrew Speakers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.44 (.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.013-(-).016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e*Arabic speakers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.49 (.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.49 (.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.010-.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced/Widowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.54 (.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.024-.184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.130\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e*Single\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.33 (.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcademic degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.48 (.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.083-.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVocational degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.54 (.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.071-.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e*Matriculations or less\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.36 (.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.007-.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of children\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.029-.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.390\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e*Reference category\u003c/p\u003e \u003cp\u003ePairwise comparisons showed no significant difference between 'Married' and 'Divorced/ Widowed' categories (p\u0026thinsp;=\u0026thinsp;.857), and between 'Academic degree' and 'Vocational degree' (p\u0026thinsp;=\u0026thinsp;.758)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDividing the M-SPFL into levels showed that approximately 50% of the sample had sufficient levels of FL, and 50% had problematic or inadequate levels (i.e., non-sufficient levels) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). No difference was seen between sectors (p\u0026thinsp;=\u0026thinsp;.449, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). Multinomial regression showed that age and being married were predictors for inadequate FL levels, and Arab sector, increased age, and lower education level are predictors for problematic FL levels, both in comparison to sufficient FL levels (Supplement 4).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study aimed to validate and shorten an extended SPFL scale in both Hebrew and Arabic. This process resulted in the M-SPFL, a 23-item scale covering 6 distinct FL domains: food preparation skills, resilience and resistance, healthy snack styles, examining food labels and daily food planning, healthy budgeting, and healthy food stockpiling. The M-SPFL demonstrated good internal consistency and acceptable model fit across both Hebrew and Arabic-speaking populations, suggesting its utility as a culturally appropriate tool for measuring FL in Israel's diverse population, while reducing respondent burden.\u003c/p\u003e \u003cp\u003eThe validation process included face validity and pretesting of an extended 35-item SPFL scale (original 29-item SPFL and additional 6 new items) as the first step, followed next by content validation. The 29-item original SPFL showed acceptable levels of fit (shown by CFA) in both Hebrew and Arabic speaking sub-populations. This indicated that the original scale is compatible with the conceptual structure. The subsequent exploratory process aimed to explore the properties and factor division of the original construct among Israeli women, as well as to shorten the tool. During this process, 12 items were removed, including an entire domain from the original SPFL, social and conscious eating. Although social and conscious eating plays an important role in the concept of FL [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], this study indicates that Israeli women do not perceive it as part of the content realm of the rest of the scale items, viewing it as a different construct, as expressed by the low loading factors of the domain items. This is similar to the findings of Luque et al.\u0026rsquo;s Spanish SPFL validation study [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], which also eliminated the social and conscious eating domain due to low significance. Luque et al. explained this as a result of the characteristics of their study population (busy and time restricted academic university students), but also highlighted the importance of geography and culture differences on this concept [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. It is also important to note that in the original SPFL development and validation study, this domain had a Cronbach\u0026rsquo;s alpha just below the acceptable value of 0.7, and this was also seen in a Turkish SPFL validation study (domain Cronbach\u0026rsquo;s alpha\u0026thinsp;=\u0026thinsp;0.61)[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, in our professional opinion, social and conscious eating is an important factor that has an effect on eating habits and FL level. Therefore, we suggest adding at least one question from this domain (question 19, Supplement 1) as a covariate, when administering questionnaires measuring FL levels in Israeli women.\u003c/p\u003e \u003cp\u003eA further difference in the domains, in addition to the elimination of the social and conscious eating domain, was the merging of two domains- examining food labels and daily food planning. This restructuring of factors was also seen in Luque's study, who chose to name this combined domain \u0026lsquo;nutritional literacy and planning\u0026rsquo;. Although understanding of food labelling information appears in the \u0026lsquo;Select' domain of the Vidgen Gallegos model and not the \u0026lsquo;Plan and Manage domain [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], one can understand how the selection of healthier products can also be seen as part of planning and prioritizing healthy food intake.\u003c/p\u003e \u003cp\u003eOf the 6 new items added to the original 29-item scale, 4 remained and were placed appropriately in the 6 domains. CFA showed acceptable levels of fit for the 23-item M-SPFL, for the whole sample and for each sector. The internal consistency showed adequate reliability for each of the domains and for the overall scale (CR\u0026thinsp;=\u0026thinsp;0.89, slightly higher than that of the 29-item scale, CR\u0026thinsp;=\u0026thinsp;0.85), and was comparable to the level of internal consistency observed in other food literacy scales (Cronbach's alphas of 0.83 [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], 0.82 [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], and 0.89 [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]). The mean score of the 23-item M-SPFL (3.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58, scale range 1\u0026ndash;5), as well as the mean score of the 29-item SPFL (3.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48), in our sample was slightly lower than that seen in the original SPFL validation sample [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] (3.833.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41), even though the samples are similar in gender, age, and education level. This may suggest that the lower FL levels measured in the current study are due to core population disparities, stemming from culture, the Israeli physical and social environment and policy differences.\u003c/p\u003e \u003cp\u003eFinally, the M-SPFL showed positive correlation with adherence to the Mediterranean diet (measured using the I-MEDAS [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]), consistent with previous studies [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], indicating its convergent validity.\u003c/p\u003e \u003cp\u003eThis study measured FL levels among a large number of diverse Israeli women, providing stakeholders with the information to make informed public health policies according to sub populations needs regarding healthy nutrition. Approximately 50% of respondents had insufficient FL levels, which highlights the need for action. The increased FL levels with age, seen in other studies as well [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], suggests that FL skills may develop over time through accumulated experience. The association between higher education and increased FL scores, were also similar to findings in other studies, [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e] and highlight potential disparities in food-related knowledge, skills and opportunities that merit attention from nutrition and public health professionals. The finding that married women scored higher than single women may reflect the influence of family responsibilities on food-related behaviors and skills development, or just a greater opportunity to put such skills to use. Notably, among Arabic speakers no significant correlation was seen between marital status and M-SPFL, but this may be due to the small number of unmarried participants in this sub-group (only 42 singles).\u003c/p\u003e \u003cp\u003eWhile bivariate analyses showed no significant differences between the Arabic and Hebrew speaking populations when examining both the mean score and the categorization to M-SPFL levels, regression models showed slightly higher scores for Arabic speakers after adjusting for socio-demographic variables. When examining each FL domain between sectors, the M-SPFL performed similarly across Hebrew and Arabic-speaking populations at the construct level, though differences emerged at the scale level. This suggests that whereas the tool measures the same underlying constructs in both populations, there may be cultural variations in how these skills and behaviors manifest. As the M-SPFL measures perceptions of FL skills and not actual behaviours, there may be a tendency among the Arabic sector to positively view their FL related behaviours, even though they report lower adherence to the Mediterranean diet (I-MEDAS scores are slightly lower among Arabic speakers, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). In general, Israeli Arabs tend to report higher levels of self-rated health compared to Israeli Jews [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Several Israeli studies showed that Arab participants reported higher self-rated health, although their long-term survival rate is significantly lower and they are disadvantaged according to most health indicators [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. This discrepancy has been previously explained [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e] by suggesting the two sectors may not have the same meaning in relation to objective measures of health, as cultural differences may play a role in health perceptions of different populations. This may explain the gap between reported perceived nutritional-related behaviours and actual food consumption. This finding once again underscores the importance of considering cultural context when developing and implementing FL interventions and policies [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Strengths and Limitations\u003c/h2\u003e \u003cp\u003eSeveral limitations of this study should be noted. The social media recruitment strategy may have resulted in selection bias, particularly toward more educated participants, despite efforts to recruit across various education levels. Additionally, self-reported measures may be subject to bias. As this study focused on women over 25 years old, this study cannot be generalized to younger ages or to men. Investigation of FL among other population groups, including men and young adults, will provide valuable additional insights and is recommended as the next step.\u003c/p\u003e \u003cp\u003eThe study also has several strengths, including its large sample size, robust validation process, and inclusion of both Hebrew and Arabic-speaking populations while ensuring cultural sensitivity through the face validity and pretest process. Finally, the use of a validated tool enables future comparisons with other international studies.\u003c/p\u003e \u003cp\u003eFuture research should examine the M-SPFL's predictive validity for dietary behaviors and health outcomes, as well as its sensitivity to change in intervention studies.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThe 23-item M-SPFL represents a valid and reliable tool for measuring FL among Israeli women, with potential applications in both research and practice. This shorter version of the 29-item SFPL, together with culturally sensitization, make it particularly suitable for use in community-based settings and public health programs aimed at improving dietary behaviors and health outcomes in Israel's diverse population. The strong correlation between the M-SPFL scale and the I-MEDAS underscores the importance of focusing on FL skills and competencies as an integral part of any intervention targeting nutritional change.\u003c/p\u003e \u003cp\u003eThe findings in this study have important implications for nutrition and public health practice in Israel; the validated M-SPFL scale constitutes a reliable tool for assessing FL, which can inform the development and evaluation of targeted interventions. This adapted measure has been shortened yet incorporates additional items, which may enable similar measures to be validated in other geographic regions. Different socio-demographic FL findings suggest that interventions should be tailored to different population segments, particularly those with lower education levels and younger adults, in order to reduce disparities and increase food literacy in different populations.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eFL: food literacy; SPFL: short perceived food literacy scale; M-SPFL: modified short perceived food literacy scale; MEDAS: Mediterranean diet adherence screener; I-MEDAS: Israeli Mediterranean diet adherence screener, CFA: confirmatory factor analysis; EFA: exploratory factor analysis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate declaration:\u0026nbsp;\u003c/strong\u003eThe study was approved by the Institutional Ethics Committee of Hadassah University Medical Center (HMO-0135-19, approved March 2019). All study participants provided their written consent to participate in the study. All methods were carried out in accordance with relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability:\u0026nbsp;\u003c/strong\u003eData will be made available on request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no competing interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ Contributions:\u003c/strong\u003e Keren L. Greenberg: Writing – original draft, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Yael Bar-Zeev: Writing – review \u0026amp; editing, Supervision, Methodology, Formal analysis, Conceptualization. Milka Donchin: Writing – review \u0026amp; editing, Supervision, Methodology, Formal analysis, Conceptualization. Donna R. Zwas: Writing – review \u0026amp; editing, Supervision, Methodology, Formal analysis, Conceptualization.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e The authors would like to thank the experts who took part in the face validity process for their valuable feedback, all the participants who filled out the pre-test and online survey for their contribution, and Dr. Amir Hefetz and Dr. Gabi Liberman for statistical assistance and input.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eVidgen HA, Gallegos D. Defining food literacy and its components. Appetite. 2014;76:50\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.appet.2014.01.010\u003c/span\u003e\u003cspan address=\"10.1016/j.appet.2014.01.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCullen T, Hatch J, Martin W, Higgins JW, Sheppard R. Food literacy: Definition and framework for action. Can J Diet Pract Res. 2015;76(3):140\u0026ndash;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3148/cjdpr-2015-010\u003c/span\u003e\u003cspan address=\"10.3148/cjdpr-2015-010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKrause C, Sommerhalder K, Beer-Borst S, Abel T. Just a subtle difference? Findings from a systematic review on definitions of nutrition literacy and food literacy. Health Promot Int. 2018;33(3):378\u0026ndash;89. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/heapro/daw084\u003c/span\u003e\u003cspan address=\"10.1093/heapro/daw084\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHernandez KJ, Gillis D, Kevany K, Kirk S. Towards a common understanding of food literacy: A pedagogical framework. Can Food Stud. 2021;8(4). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.15353/cfs-rcea.v8i4.467\u003c/span\u003e\u003cspan address=\"10.15353/cfs-rcea.v8i4.467\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAzevedo Perry E, Thomas H, Samra HR, Edmonstone S, Davidson L, Faulkner A, Petermann L, Manaf\u0026ograve; E, Kirkpatrick SI. Identifying attributes of food literacy: A scoping review. Public Health Nutr. 2017;20(13):2406\u0026ndash;15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/S1368980017001276\u003c/span\u003e\u003cspan address=\"10.1017/S1368980017001276\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRosas R, Pimenta F, Leal I, Schwarzer R. FOODLIT-PRO: Conceptual and empirical development of the food literacy wheel. Int J Food Sci Nutr. 2021;72(1):99\u0026ndash;111. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/09637486.2020.1762547\u003c/span\u003e\u003cspan address=\"10.1080/09637486.2020.1762547\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTruman E, Lane D, Elliott C. Defining food literacy: A scoping review. Appetite. 2017;116:365\u0026ndash;71. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.appet.2017.05.007\u003c/span\u003e\u003cspan address=\"10.1016/j.appet.2017.05.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmouzandeh C, Fingland D, Vidgen HA. A Scoping review of the validity, reliability and conceptual alignment of food literacy measures for adults. Nutrients. 2019;11(4):801. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/nu11040801\u003c/span\u003e\u003cspan address=\"10.3390/nu11040801\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePoelman MP, Dijkstra SC, Sponselee H, Kamphuis CBM, Battjes-Fries MCE, Gillebaart M, Seidell JC. Towards the measurement of food literacy with respect to healthy eating: The development and validation of the self perceived food literacy scale among an adult sample in the Netherlands. Int J Behav Nutr Phys Act. 2018;15(1):54. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12966-018-0687-z\u003c/span\u003e\u003cspan address=\"10.1186/s12966-018-0687-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoslooper-Meulenbelt K, Boonstra MD, van Vliet IMY, Gomes-Neto AW, Ost\u0026eacute; MCJ, Poelman MP, Bakker SJL, de Winter AF, Navis GJ. Food literacy is associated with adherence to a Mediterranean-style diet in kidney transplant recipients. J Ren Nutr. 2021;31(6):628\u0026ndash;36. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1053/j.jrn.2020.12.010\u003c/span\u003e\u003cspan address=\"10.1053/j.jrn.2020.12.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee Y, Kim T, Jung H. Effects of university students\u0026rsquo; perceived food literacy on ecological eating behavior towards sustainability. Sustainability. 2022;14(9):5242. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.3390/su14095242\u003c/span\u003e\u003cspan address=\"10.3390/su14095242\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuque B, Villa\u0026eacute;cija J, Ramallo A, de Matos MG, Castillo-May\u0026eacute;n R, Cuadrado E, Tabernero C. Spanish validation of the self-perceived food literacy scale: A Five-factor model proposition. Nutrients. 2022;14(14):2902. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/nu14142902\u003c/span\u003e\u003cspan address=\"10.3390/nu14142902\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSel\u0026ccedil;uk KT, \u0026Ccedil;evik C, Baydur H, Meseri R. Validity and reliability of the Turkish version of the self-perceived food literacy scale. Prog Nutr. 2020;22:671\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.23751/pn.v22i2.9662\u003c/span\u003e\u003cspan address=\"10.23751/pn.v22i2.9662\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSponselee HCS, Kroeze W, Poelman MP, Renders CM, Ball K, Steenhuis IHM. Food and health promotion literacy among employees with a low and medium level of education in the Netherlands. BMC Public Health. 2021;21(1):1273. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12889-021-11322-6\u003c/span\u003e\u003cspan address=\"10.1186/s12889-021-11322-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTrieste L, Bazzani A, Amato A, Faraguna U, Turchetti G. Food literacy and food choice\u0026ndash;a survey-based psychometric profiling of consumer behaviour. Br Food J. 2021;123(13):124\u0026ndash;41. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1108/BFJ-09-2020-0845\u003c/span\u003e\u003cspan address=\"10.1108/BFJ-09-2020-0845\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZastrow F, Neher K, Pentner C, Hassel H. Eating an enjoyable and balanced diet\u0026ndash;food literacy among older adults. Ernahr Umsch. 2021;68(3):53\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoedt T, Steenackers N, Verbeke J, Vermeulen A, De Backer C, Yiga P, Matthys C. A Mixed-method approach to develop and validate an integrated food literacy tool for personalized food literacy guidance. Front Nutr. 2022;8:760493. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fnut.2021.760493\u003c/span\u003e\u003cspan address=\"10.3389/fnut.2021.760493\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePark D, Park YK, Park CY, Choi MK, Shin MJ. Development of a comprehensive food literacy measurement tool integrating the food system and sustainability. Nutrients. 2020;12(11):3300. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/nu12113300\u003c/span\u003e\u003cspan address=\"10.3390/nu12113300\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePaynter E, Begley A, Butcher LM, Dhaliwal SS. The validation and improvement of a food literacy behavior checklist for food literacy programs. Int J Environ Res Public Health. 2021;18(24):13282. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijerph182413282\u003c/span\u003e\u003cspan address=\"10.3390/ijerph182413282\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRosas R, Pimenta F, Leal I, Schwarzer R. FOODLIT-tool: Development and validation of the adaptable food literacy tool towards global sustainability within food systems. Appetite. 2022;168:105658. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.appet.2021.105658\u003c/span\u003e\u003cspan address=\"10.1016/j.appet.2021.105658\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSo H, Park D, Choi MK, Kim YS, Shin MJ, Park YK. Development and validation of a food literacy assessment tool for community-dwelling elderly people. Int J Environ Res Public Health. 2021;18(9):4979. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijerph18094979\u003c/span\u003e\u003cspan address=\"10.3390/ijerph18094979\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThompson C, Byrne R, Adams J, Vidgen HA. Development, validation and item reduction of a food literacy questionnaire (IFLQ-19) with Australian adults. Int J Behav Nutr Phys Act. 2022;19(1):113. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12966-022-01351-8\u003c/span\u003e\u003cspan address=\"10.1186/s12966-022-01351-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoo H, Jo E, Lee H, Park S. Development of a food literacy assessment tool for healthy, joyful, and sustainable diet in South Korea. Nutrients. 2022;14(7):1507. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/nu14071507\u003c/span\u003e\u003cspan address=\"10.3390/nu14071507\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Y, Zhang Z, Xu M, Aihemaitijiang S, Ye C, Zhu W, Ma G. Development and validation of a food and nutrition literacy questionnaire for Chinese adults. Nutrients. 2022;14(9):1933. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/nu14091933\u003c/span\u003e\u003cspan address=\"10.3390/nu14091933\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGreenberg KL, Bar-Zeev Y, Donchin M, Karjawally M, Sneineh SA, Husseini MN, Zwas DR. Feasibility, acceptability and preliminary effectiveness of a manualized lay-led food literacy intervention for women in a community setting. Appetite. 2025;207:107885. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/J.APPET.2025.107885\u003c/span\u003e\u003cspan address=\"10.1016/J.APPET.2025.107885\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBegley A, Paynter E, Dhaliwal SS. Evaluation tool development for food literacy programs. Nutrients. 2018;10(11):1617. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/nu10111617\u003c/span\u003e\u003cspan address=\"10.3390/nu10111617\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWijayaratne SP, Reid M, Westberg K, Worsley A, Mavondo F. Food literacy, healthy eating barriers and household diet. Eur J Mark. 2018;52(12):2449\u0026ndash;77. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1108/EJM-10-2017-0760\u003c/span\u003e\u003cspan address=\"10.1108/EJM-10-2017-0760\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGr\u0026eacute;a Krause C, Beer-Borst S, Sommerhalder K, Hayoz S, Abel T. A short food literacy questionnaire (SFLQ) for adults: Findings from a Swiss validation study. Appetite. 2018;120:275\u0026ndash;80. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.appet.2017.08.039\u003c/span\u003e\u003cspan address=\"10.1016/j.appet.2017.08.039\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSamuel H, Maoz Breuer R. Food consumption habits and attitudes to the nutrition labeling program. Myers-JDC-Brookdale Institute. 2020. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://brookdale.jdc.org.il/en/publication/food-consumption-habits-and-attitudes-to-nutrition-labeling-program/\u003c/span\u003e\u003cspan address=\"https://brookdale.jdc.org.il/en/publication/food-consumption-habits-and-attitudes-to-nutrition-labeling-program/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 24 April 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIsrael Center for Disease Control, Ministry of Health. Rav Mabat adult second national health and nutritional survey, ages 18\u0026ndash;64, 2014\u0026ndash;2016. Publication 383. 2019. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gov.il/BlobFolder/reports/mabat-adults-2014-2016-383/en/files_publications_units_ICDC_mabat_adults_2014_2016_383_en.pdf\u003c/span\u003e\u003cspan address=\"https://www.gov.il/BlobFolder/reports/mabat-adults-2014-2016-383/en/files_publications_units_ICDC_mabat_adults_2014_2016_383_en.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIsrael Center for Disease Control, Ministry of Health. Israel National Health Interview Survey INHIS-4, selected findings 2018\u0026ndash;2020. 2022. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gov.il/en/pages/02082022-02\u003c/span\u003e\u003cspan address=\"https://www.gov.il/en/pages/02082022-02\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 24 April 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKalter-Leibovici O, Chetrit A, Lubin F, Atamna A, Alpert G, Ziv A, Abu-Saad K, Murad H, Eilat-Adar S, Goldbourt U. Adult-onset diabetes among Arabs and Jews in Israel: A population-based study. Diabet Med. 2012;29(6):748\u0026ndash;54. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/j.1464-5491.2011.03516.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1464-5491.2011.03516.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLake AA, Hyland RM, Mathers JC, Rugg-Gunn AJ, Wood CE, Adamson AJ. Food shopping and preparation among the 30‐somethings: Whose job is it? (The ASH30 study). Br Food J. 2006;108(6):475\u0026ndash;86. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1108/00070700610668441\u003c/span\u003e\u003cspan address=\"10.1108/00070700610668441\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReid M, Worsley A, Mavondo F. The obesogenic household: Factors influencing dietary gatekeeper satisfaction with family diet. Psychol Mark. 2015;32(5):544\u0026ndash;57. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/mar.20799\u003c/span\u003e\u003cspan address=\"10.1002/mar.20799\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWijayaratne S, Westberg K, Reid M, Worsley A. A qualitative study exploring the dietary gatekeeper's food literacy and barriers to healthy eating in the home environment. Health Promot J Austr. 2021;32(Suppl 2):292\u0026ndash;300. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/hpja.398\u003c/span\u003e\u003cspan address=\"10.1002/hpja.398\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuin\u0026eacute; RPF, Floren\u0026ccedil;a SG, Apar\u0026iacute;cio G, Cardoso AP, Ferreira M. food literacy scale: Validation through exploratory and confirmatory factor analysis in a sample of Portuguese university students. Nutrients. 2022;15(1):166. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/nu15010166\u003c/span\u003e\u003cspan address=\"10.3390/nu15010166\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDernini S, Berry EM, Serra-Majem L, La Vecchia C, Capone R, Medina FX, Aranceta-Bartrina J, Belahsen R, Burlingame B, Calabrese G, Corella D, Donini LM, Lairon D, Meybeck A, Pekcan AG, Piscopo S, Yngve A, Trichopoulou A. Med Diet 4.0: The Mediterranean diet with four sustainable benefits. Public Health Nutr. 2017;20(7):1322\u0026ndash;30. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1017/S1368980016003177\u003c/span\u003e\u003cspan address=\"10.1017/S1368980016003177\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsang S, Royse CF, Terkawi AS. Guidelines for developing, translating, and validating a questionnaire in perioperative and pain medicine. Saudi J Anaesth. 2017;11(Suppl 1):S80\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4103/sja.SJA_203_17\u003c/span\u003e\u003cspan address=\"10.4103/sja.SJA_203_17\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHefetz A, Liberman G. The factor analysis procedure for exploration: A short guide with examples. Cult Edu. 2017;29(3):526\u0026ndash;62. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/11356405.2017.1365425\u003c/span\u003e\u003cspan address=\"10.1080/11356405.2017.1365425\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIsraeli Center Bureau of Statistics. Israel in figures: Selected data from the statistical abstract of Israel 2021. 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBezeq. The state of internet in Israel Report 2022. 2022. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://media.bezeq.co.il/pdf/internetreport_2022.pdf [Hebrew]\u003c/span\u003e\u003cspan address=\"https://media.bezeq.co.il/pdf/internetreport_2022.pdf [Hebrew]\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIsraeli Central Bureau of Statistics. Education level of persons aged 25\u0026ndash;66 according to the CBS education register, 2009\u0026ndash;2022. 2025. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cbs.gov.il/he/mediarelease/DocLib/2025/040/06_25_040b.pdf [Hebrew]\u003c/span\u003e\u003cspan address=\"https://www.cbs.gov.il/he/mediarelease/DocLib/2025/040/06_25_040b.pdf [Hebrew]\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDewitt J, Capistrant B, Kohli N, Rosser BRS, Mitteldorf D, Merengwa E, West W. Addressing participant validity in a small internet health survey (The Restore Study): Protocol and recommendations for survey response validation. JMIR Res Protoc. 2018;7(4):e96. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2196/resprot.7655\u003c/span\u003e\u003cspan address=\"10.2196/resprot.7655\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHauser DJ, Ellsworth PC, Gonzalez R. Are manipulation checks necessary? Front Psychol. 2018;9:998. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fpsyg.2018.00998\u003c/span\u003e\u003cspan address=\"10.3389/fpsyg.2018.00998\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeffner JL, Watson NL, Dahne J, Croghan I, Kelly MM, McClure JB, Bars M, Thrul J, Meier E. Recognizing and preventing participant deception in online nicotine and tobacco research studies: Suggested tactics and a call to action. Nicotine Tob Res. 2021;23(10):1810\u0026ndash;2. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/ntr/ntab077\u003c/span\u003e\u003cspan address=\"10.1093/ntr/ntab077\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbu-Saad K, Endevelt R, Goldsmith R, Shimony T, Nitsan L, Shahar DR, Keinan-Boker L, Ziv A, Kalter-Leibovici O. Adaptation and predictive utility of a Mediterranean diet screener score. Clin Nutr. 2019;38(6):2928\u0026ndash;35. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.clnu.2018.12.034\u003c/span\u003e\u003cspan address=\"10.1016/j.clnu.2018.12.034\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchr\u0026ouml;der H, Fit\u0026oacute; M, Estruch R, Mart\u0026iacute;nez-Gonz\u0026aacute;lez MA, Corella D, Salas-Salvad\u0026oacute; J, Lamuela-Ravent\u0026oacute;s R, Ros E, Salaverr\u0026iacute;a I, Fiol M, Lapetra J, Vinyoles E, G\u0026oacute;mez-Gracia E, Lahoz C, Serra-Majem L, Pint\u0026oacute; X, Ruiz-Gutierrez V, Covas MI. A short screener is valid for assessing Mediterranean diet adherence among older Spanish men and women. J Nutr. 2011;141(6):1140\u0026ndash;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3945/jn.110.135566\u003c/span\u003e\u003cspan address=\"10.3945/jn.110.135566\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrown TA. Confirmatory factor analysis for applied research, second edition. Guilford Publications. 2015.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOsborne JW. Best practice in exploratory factor analysis. Createspace publishing. 2014.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarsh HW, Hau KT, Wen Z. In Search of golden rules: Comment on hypothesis-testing approaches to setting cutoff values for fit indexes and dangers in overgeneralizing Hu and Bentler\u0026rsquo;s (1999) findings. Struct Equ Model. 2004;11(3), 320\u0026ndash;341. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1207/s15328007sem1103_2\u003c/span\u003e\u003cspan address=\"10.1207/s15328007sem1103_2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSamuels P. Scale formation: Scale reliability analysis and exploratory factor analysis. In Researching and Analysing Business: Research Methods in Practice Taylor and Francis. 2023 (pp. 283\u0026ndash;294). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4324/9781003107774-22\u003c/span\u003e\u003cspan address=\"10.4324/9781003107774-22\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHenseler J, Ringle CM, Sarstedt M. A new criterion for assessing discriminant validity in variance-based structural equation modeling. J Acad Mark Sci. 2015;43:115\u0026ndash;35. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11747-014-0403-8\u003c/span\u003e\u003cspan address=\"10.1007/s11747-014-0403-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaykov T. Estimation of composite reliability for congeneric measures. 1997;21(2),173\u0026ndash;84. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/01466216970212006\u003c/span\u003e\u003cspan address=\"10.1177/01466216970212006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDunn TJ, Baguley T, Brunsden V. From alpha to omega: A practical solution to the pervasive problem of internal consistency estimation. 2014;105(3):399\u0026ndash;412. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/bjop.12046\u003c/span\u003e\u003cspan address=\"10.1111/bjop.12046\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKolpatzik K. Ern\u0026auml;hrungskompetenz in Deutschland. In Gesundheitskompetenz. Heidelberg: Springer Berlin Heidelberg; 2022. pp. 1\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDesjardins E, Azevedo E, Davidson L, Samra R, MacDonald A, Dunbar J, Thomas H, Munoz MA, King B, Maxwell T, Wong-McGraw P, Shukla R, Traynor M. Making something out of nothing: Food literacy among youth, young pregnant women and young parents who are at risk for poor health, a locally driven collaborative project of Public Health Ontario. 2013.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePalumbo R, Adinolfi P, Annarumma C, Catinello G, Tonelli M, Troiano E, Vezzosi S, Manna R. Unravelling the food literacy puzzle: Evidence from Italy. Food Policy. 2019;83:104\u0026ndash;15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/J.FOODPOL.2018.12.004\u003c/span\u003e\u003cspan address=\"10.1016/J.FOODPOL.2018.12.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShafran I, Benyamini Y, Keinan-Boker L, Gerber Y. Self-rated health and mortality among older adults in Israel: A comparison between Jewish and Arab Populations. J Clin Med. 2024;13(22):6978. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/jcm13226978\u003c/span\u003e\u003cspan address=\"10.3390/jcm13226978\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRozani V. Ethnic differences in socioeconomic and health determinants related to self-rated health status: A study on community-dwelling Israeli Jews and Arabs in old age. Int J Environ Res Public Health. 2022;19(20):13660. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijerph192013660\u003c/span\u003e\u003cspan address=\"10.3390/ijerph192013660\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaron-Epel O, Kaplan G, Haviv-Messika A, Tarabeia J, Green MS, Kaluski DN. Self-reported health as a cultural health determinant in Arab and Jewish Israelis MABAT\u0026ndash;National Health and Nutrition Survey 1999\u0026ndash;2001. Soc Sci Med. 2005;61(6):1256\u0026ndash;66. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/J.SOCSCIMED.2005.01.022\u003c/span\u003e\u003cspan address=\"10.1016/J.SOCSCIMED.2005.01.022\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Food literacy, Scale validation, Mediterranean diet, Public health nutrition, Women’s nutrition","lastPublishedDoi":"10.21203/rs.3.rs-6644848/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6644848/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eFood literacy (FL) encompasses the knowledge, skills, and behaviors required for making informed food choices. The short-perceived food literacy scale (SPFL, 29 items) is a widely used FL measurement tool, yet it has not been validated and adapted for the diverse Israeli population. This study aims to validate and shorten an adapted SPFL for Hebrew and Arabic-speaking women in Israel, ensuring cultural relevance and reducing respondent burden.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe validation process comprised three steps: face validity and pretesting of an extended 35-item SPFL, content validation via confirmatory factor analysis (CFA) and exploratory factor analysis (EFA) on survey data including 2,129 participants (653 Arabic speakers, 1,476 Hebrew speakers), and convergent validity assessment through correlation with the Israeli Mediterranean Diet Adherence Scale (I-MEDAS). Reliability was assessed via internal consistency measures, and associations between FL levels and socio-demographic factors were also examined. CFA confirmed the original SPFL\u0026rsquo;s 8-domain structure, while EFA identified six FL domains, leading to a refined 23-item modified SPFL (M-SPFL).\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe M-SPFL demonstrated strong internal consistency (composite reliability\u0026thinsp;=\u0026thinsp;0.89) and acceptable model fit across both language groups, and was correlated with the original SPFL (r\u0026thinsp;=\u0026thinsp;0.96, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and with I-MEDAS scores (r\u0026thinsp;=\u0026thinsp;0.52, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). FL levels were positively associated with age, marital status, and higher education.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe M-SPFL is a valid, reliable, and culturally adapted tool for assessing FL among Israeli women. Its application can enhance public health initiatives by informing targeted nutrition interventions to improve dietary behaviors and reduce health disparities.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTrial registration\u003c/b\u003e\u003c/p\u003e \u003cp\u003eNot applicable.\u003c/p\u003e","manuscriptTitle":"Validation and adaptation of the short-perceived food literacy scale (SPFL) among Israeli women","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-12 06:13:15","doi":"10.21203/rs.3.rs-6644848/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-15T17:28:41+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-03T15:08:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-17T08:58:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"337146463788325505909360360176167078752","date":"2025-06-09T03:18:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"178817806317681261543129351886785807837","date":"2025-06-05T06:29:35+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-05T04:15:23+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-15T16:24:08+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-14T14:21:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-14T14:16:13+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-05-12T08:54:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d38328a3-b936-4324-adbd-9c872ec23b35","owner":[],"postedDate":"June 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-08T16:09:48+00:00","versionOfRecord":{"articleIdentity":"rs-6644848","link":"https://doi.org/10.1186/s12889-025-25597-6","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2025-12-04 15:58:04","publishedOnDateReadable":"December 4th, 2025"},"versionCreatedAt":"2025-06-12 06:13:15","video":"","vorDoi":"10.1186/s12889-025-25597-6","vorDoiUrl":"https://doi.org/10.1186/s12889-025-25597-6","workflowStages":[]},"version":"v1","identity":"rs-6644848","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6644848","identity":"rs-6644848","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}